Abstract

This paper establishes a review of the recent study in the field of hyperspectral (HS) image compression approaches. Recently, image compression techniques have achieved significant advances from diverse types of coding standards/approaches. HS image compression requires an unconventional coding technique because of its unique, multiple-dimensional structure. The data redundancy exists in both inter-band and intra-band methods. The survey summarizes current literature in inter- and intra-band compression methods. The challenges, opportunities, and future research possibilities regarding HS image compression are further discussed. The experimental results are also provided for validity and applicability of the existing HS image compression techniques.

© 2017 Optical Society of America

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References

  • View by:

  1. R. B. Smith, “Introduction to hyperspectral imaging,” Microimages, 2006, http://www.microimages.com/documentation/Tutorials/hyprspec.pdf .
  2. W. F. Good, G. S. Maitz, and D. Gur, “Joint Photographic Experts Group (JPEG) compatible data compression of mammograms,” J. Digit. Imaging 7, 123–132 (1994).
  3. A. Skodras, C. Christopoulos, and T. Ebrahimi, “The JPEG 2000 still image compression standard,” IEEE Signal Process. Mag. 18(5), 36–58 (2001).
    [Crossref]
  4. G. Motta, F. Rizzo, and J. A. Storer, Hyperspectral Data Compression (Springer, 2006).
  5. A. C. Bovik, Handbook of Image and Video Processing (Academic, 2010).
  6. G. Dudek, P. Borys, and Z. J. Grzywna, “Lossy dictionary-based image compression method,” Image Vision Comput. 25, 883–889 (2007).
    [Crossref]
  7. G. G. King, C. C. Seldev, and N. A. Singh, “A novel compression technique for compound images using parallel Lempel–Ziv–Welch algorithm,” Appl. Mech. Mater. 626, 44–51 (2014).
    [Crossref]
  8. L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, “Compression of hyperspectral remote sensing images by tensor approach,” Neurocomputing 147, 358–363 (2015).
    [Crossref]
  9. M. A. Veganzones, J. E. Cohen, R. C. Farias, J. Chanussot, and P. Comon, “Nonnegative tensor CP decomposition of hyperspectral data,” IEEE Trans. Geosci. Remote Sens. 54, 2577–2588 (2016).
    [Crossref]
  10. J. Mielikainen and B. Huang, “Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length,” IEEE Geosci. Remote Sens. Lett. 9, 1118–1121 (2012).
    [Crossref]
  11. N. R. Mat Noor and T. Vladimirova, “Investigation into lossless hyperspectral image compression for satellite remote sensing,” Int. J. Remote Sens. 34, 5072–5104 (2013).
    [Crossref]
  12. S. Shukla and M. Prasad, Lossy Image Compression: Domain Decomposition-Based Algorithms (Springer, 2011).
  13. C.-I. Chang, Hyperspectral Data Processing: Algorithm Design and Analysis (Wiley, 2013).
  14. D. Zhao, S. Zhu, and F. Wang, “Lossy hyperspectral image compression based on intra-band prediction and inter-band fractal encoding,” Comput. Electr. Eng. 54, 494–505 (2016).
    [Crossref]
  15. S. Zhu, D. Zhao, and F. Wang, “Hybrid prediction and fractal hyperspectral image compression,” Math. Probl. Eng. 2015, 950357 (2015).
    [Crossref]
  16. X. Pan, R. Liu, and X. Lv, “Low-complexity compression method for hyperspectral images based on distributed source coding,” IEEE Geosci. Remote Sens. Lett. 9, 224–227 (2012).
    [Crossref]
  17. T. Qiao, J. Ren, M. Sun, J. Zheng, and S. Marshall, “Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications,” Int. J. Remote Sens. 35, 7316–7337 (2014).
    [Crossref]
  18. I. Blanes and J. Serra-Sagristà, “Cost and scalability improvements to the Karhunen–Loêve transform for remote-sensing image coding,” IEEE Trans. Geosci. Remote Sens. 48, 2854–2863 (2010).
    [Crossref]
  19. Y. Nian, Y. Liu, and Z. Ye, “Pairwise KLT-based compression for multispectral images,” Sens. Imaging 17, 1–15 (2016).
    [Crossref]
  20. S. Shahriyar, M. Paul, M. Murshed, and M. Ali, “Lossless hyperspectral image compression using binary tree based decomposition,” in International Conference on Digital Image Computing: Techniques and Applications (DICTA) (IEEE, 2016), pp. 1–8.
  21. Q. Du and J. E. Fowler, “Hyperspectral image compression using JPEG2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4, 201–205 (2007).
    [Crossref]
  22. L. Wang, J. Wu, L. Jiao, and G. Shi, “Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT,” IEEE Geosci. Remote Sens. Lett. 6, 587–591 (2009).
    [Crossref]
  23. Q. Du, N. Ly, and J. E. Fowler, “An operational approach for hyperspectral image compression,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1357–1360.
  24. B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
    [Crossref]
  25. F. L. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,” Stud. Appl. Math. 6, 164–189 (1927).
    [Crossref]
  26. L. R. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika 31, 279–311 (1966).
    [Crossref]
  27. A. Karami, R. Heylen, and P. Scheunders, “Hyperspectral image compression optimized for spectral unmixing,” IEEE Trans. Geosci. Remote Sens. 54, 5884–5894 (2016).
    [Crossref]
  28. R. A. Harshman, “Foundations of the PARAFAC procedure: models and conditions for an ‘explanatory’ multi-modal factor analysis,” in UCLA Working Papers in Phonetics (1970), Vol. 16, pp. 1–84.
  29. J. D. Carroll and J.-J. Chang, “Analysis of individual differences in multidimensional scaling via an N-way generalization of ‘Eckart–Young’ decomposition,” Psychometrika 35, 283–319 (1970).
    [Crossref]
  30. K. Sayood, Introduction to Data Compression (Newnes, 2012).
  31. “Vector quantization,” 2016, http://www.data-compression.com/vq.html .
  32. M. F. M. Salleh and J. Soraghan, “A new multistage lattice vector quantization with adaptive subband thresholding for image compression,” EURASIP J. Adv. Signal Process. 2007, 092928 (2007).
    [Crossref]
  33. J. Zhang, Y. Li, K. Wang, and H. Liu, “The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate,” Proc. SPIE 8514, 85140W (2012).
    [Crossref]
  34. X. J. Zhao and Y. F. Jing, “The application of vector quantization algorithm in hyperspectral image compression,” in Advanced Materials Research (Trans Tech Publication, 2013), pp. 1479–1483.
  35. X. Li, J. Ren, C. Zhao, T. Qiao, and S. Marshall, “Novel multivariate vector quantization for effective compression of hyperspectral imagery,” Opt. Commun. 332, 192–200 (2014).
    [Crossref]
  36. B. Rasti, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, “Hyperspectral image denoising using 3D wavelets,” in IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1349–1352.
  37. J. Gonzalez-Conejero, J. Bartrina-Rapesta, and J. Serra-Sagrista, “JPEG2000 encoding of remote sensing multispectral images with no-data regions,” IEEE Geosci. Remote Sens. Lett. 7, 251–255 (2010).
    [Crossref]
  38. H. Shen, W. D. Pan, and D. Wu, “Predictive lossless compression of regions of interest in hyperspectral images with no-data regions,” IEEE Trans. Geosci. Remote Sens. 55, 173–182 (2017).
    [Crossref]
  39. V. Shingate, T. Sontakke, and S. Talbar, “Still image compression using embedded zerotree wavelet encoding,” Int. J. Comput. Sci. Commun. 1, 21–24 (2010).
  40. R. George and M. Manimekalai, “A novel approach for image compression using zero tree coding,” in International Conference on Electronics and Communication Systems (ICECS) (IEEE, 2014), pp. 1–5.
  41. M. Zala and S. Parmar, “3D Wavelet transform with SPIHT algorithm for image compression,” Int. J. Appl. Innov. Eng. Manage. 2, 384–392 (2013).
  42. X. Tang and W. A. Pearlman, “Three-dimensional wavelet-based compression of hyperspectral images,” in Hyperspectral Data Compression (Springer, 2006), pp. 273–308.
  43. N. Amrani, J. Serra-Sagristà, V. Laparra, M. W. Marcellin, and J. Malo, “Regression wavelet analysis for lossless coding of remote-sensing data,” IEEE Trans. Geosci. Remote Sens. 54, 5616–5627 (2016).
    [Crossref]
  44. A. Karami, M. Yazdi, and G. Mercier, “Compression of hyperspectral images using discerete wavelet transform and Tucker decomposition,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 444–450 (2012).
    [Crossref]
  45. X. Tang, W. A. Pearlman, and J. W. Modestino, “Hyperspectral image compression using three-dimensional wavelet coding,” Proc. SPIE 5022, 1037–1047 (2003).
    [Crossref]
  46. H. H. Zayed, S. E. Kishk, and H. M. Ahmed, “3D wavelets with SPIHT coding for integral imaging compression,” Int. J. Comput. Sci. Netw. Secur. 12, 126–133 (2012).
  47. X. Tang, S. Cho, and W. A. Pearlman, “3D set partitioning coding methods in hyperspectral image compression,” in International Conference on Image Processing ICIP (IEEE, 2003), p. II-239.
  48. X. Wu and N. Memon, “Context-based lossless interband compression-extending CALIC,” IEEE Trans. Image Process. 9, 994–1001 (2000).
    [Crossref]
  49. G. Ulacha and R. Stasiński, “New context-based adaptive linear prediction algorithm for lossless image coding,” in International Conference on Signals and Electronic Systems (ICSES) (IEEE, 2014), pp. 1–4.
  50. E. Magli, G. Olmo, and E. Quacchio, “Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC,” IEEE Geosci. Remote Sens. Lett. 1, 21–25 (2004).
    [Crossref]
  51. Y. Qian, M. Ye, and J. Zhou, “Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features,” IEEE Trans. Geosci. Remote Sens. 51, 2276–2291 (2013).
    [Crossref]
  52. I. Ülkü and B. U. Töreyin, “Lossy compression of hyperspectral images using online learning based sparse coding,” in International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM) (IEEE, 2014), pp. 1–5.
  53. Y. Qian and M. Ye, “Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 6, 499–515 (2013).
    [Crossref]
  54. A. S. Charles, B. A. Olshausen, and C. J. Rozell, “Learning sparse codes for hyperspectral imagery,” IEEE J. Sel. Top. Signal Process. 5, 963–978 (2011).
    [Crossref]
  55. I. Ülkü and B. U. Töreyin, “Sparse coding of hyperspectral imagery using online learning,” Signal Image Video Process. 9, 959–966 (2015).
    [Crossref]
  56. A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro, “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 49, 4263–4281 (2011).
    [Crossref]
  57. W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).
  58. C.-C. Lin and Y.-T. Hwang, “Lossless compression of hyperspectral images using adaptive prediction and backward search schemes,” J. Inf. Sci. Eng. 27, 419–435 (2011).
  59. C.-C. Lin and Y.-T. Hwang, “An efficient lossless compression scheme for hyperspectral images using two-stage prediction,” IEEE Geosci. Remote Sens. Lett. 7, 558–562 (2010).
    [Crossref]
  60. L. Santos, S. Lopez, G. M. Callico, J. F. Lopez, and R. Sarmiento, “Performance evaluation of the H. 264/AVC video coding standard for lossy hyperspectral image compression,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 451–461 (2012).
    [Crossref]
  61. F. Zhao, G. Liu, and X. Wang, “An efficient macroblock-based diverse and flexible prediction modes selection for hyperspectral images coding,” Signal Process. 25, 697–708 (2010).
    [Crossref]
  62. F. Gao, X. Ji, C. Yan, and Q. Dai, “Compression of multispectral image using HEVC,” Proc. SPIE 9273, 92732X (2014).
    [Crossref]
  63. R. Dusselaar, M. Paul, and T. Bossomaier, “Hyperspectral image coding using spectral prediction modelling in HEVC coding framework,” in International Conference on Image and Vision Computing New Zealand (IVCNZ) (IEEE, 2015), pp. 1–6.
  64. M. Paul, R. Xiao, J. Gao, and T. Bossomaier, “Reflectance prediction modelling for residual-based hyperspectral image coding,” PloS one 11, e0161212 (2016).
    [Crossref]
  65. D. Marmanis, M. Datcu, T. Esch, and U. Stilla, “Deep learning earth observation classification using ImageNet pretrained networks,” IEEE Geosci. Remote Sens. Lett. 13, 105–109 (2016).
    [Crossref]
  66. W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
    [Crossref]
  67. N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep learning classification of land cover and crop types using remote sensing data,” IEEE Geosci. Remote Sens. Lett. 14, 778–782 (2017).
    [Crossref]
  68. A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. Remote Sens. 54, 1349–1362 (2016).
    [Crossref]
  69. W. Zhao and S. Du, “Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach,” IEEE Trans. Geosci. Remote Sens. 54, 4544–4554 (2016).
    [Crossref]
  70. Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54, 6232–6251 (2016).
    [Crossref]
  71. G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” arXiv:1608.05148 (2016).
  72. Consultative Committee for Space Data Systems (CCSDS), “Lossless data compression,” , Blue Book (1997).
  73. Consultative Committee for Space Data Systems (CCSDS), “Image data compression,” , Blue Book (2005).
  74. Consultative Committee for Space Data Systems (CCSDS), “Lossless multispectral and hyperspectral image compression,” , Blue Book (2012).
  75. C. Wang, M. Gong, M. Zhang, and Y. Chan, “Unsupervised hyperspectral image band selection via column subset selection,” IEEE Geosci. Remote Sens. Lett. 12, 1411–1415 (2015).
  76. P. K. Podder, M. Paul, and M. Murshed, “Efficient coding strategy for HEVC performance improvement by exploiting motion features,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2015), pp. 1414–1418.
  77. S. Raja and A. Suruliandi, “Image compression using WDR & ASWDR techniques with different wavelet codecs,” ACEEE Int. J. Inf. Technol. 1, 23–26 (2011).
    [Crossref]
  78. P. J. Pabich, Hyperspectral Imagery: Warfighting Through a Different Set of Eyes (Defense Technical Information Center, 2002).
  79. D. H. Foster, K. Amano, S. M. Nascimento, and M. J. Foster, “Frequency of metamerism in natural scenes,” J. Opt. Soc. Am. A 23, 2359–2372 (2006).
    [Crossref]
  80. J. Wu, Z. Wu, and C. Wu, “Lossy to lossless compressions of hyperspectral images using three-dimensional set partitioning algorithm,” Opt. Eng. 45, 027005 (2006).
    [Crossref]
  81. “Imec snapshot hyperspectral imaging camera demonstration,” 2013, https://vimeo.com/64705346 .
  82. “A breakthrough in precision farming,” 2017, https://www.questuav.com/media/case-study/multispectral-imaging-questuav-micasense-pix4dmapper-questuav-news/ .

2017 (3)

H. Shen, W. D. Pan, and D. Wu, “Predictive lossless compression of regions of interest in hyperspectral images with no-data regions,” IEEE Trans. Geosci. Remote Sens. 55, 173–182 (2017).
[Crossref]

W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).

N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep learning classification of land cover and crop types using remote sensing data,” IEEE Geosci. Remote Sens. Lett. 14, 778–782 (2017).
[Crossref]

2016 (11)

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. Remote Sens. 54, 1349–1362 (2016).
[Crossref]

W. Zhao and S. Du, “Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach,” IEEE Trans. Geosci. Remote Sens. 54, 4544–4554 (2016).
[Crossref]

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54, 6232–6251 (2016).
[Crossref]

M. Paul, R. Xiao, J. Gao, and T. Bossomaier, “Reflectance prediction modelling for residual-based hyperspectral image coding,” PloS one 11, e0161212 (2016).
[Crossref]

D. Marmanis, M. Datcu, T. Esch, and U. Stilla, “Deep learning earth observation classification using ImageNet pretrained networks,” IEEE Geosci. Remote Sens. Lett. 13, 105–109 (2016).
[Crossref]

W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
[Crossref]

A. Karami, R. Heylen, and P. Scheunders, “Hyperspectral image compression optimized for spectral unmixing,” IEEE Trans. Geosci. Remote Sens. 54, 5884–5894 (2016).
[Crossref]

N. Amrani, J. Serra-Sagristà, V. Laparra, M. W. Marcellin, and J. Malo, “Regression wavelet analysis for lossless coding of remote-sensing data,” IEEE Trans. Geosci. Remote Sens. 54, 5616–5627 (2016).
[Crossref]

M. A. Veganzones, J. E. Cohen, R. C. Farias, J. Chanussot, and P. Comon, “Nonnegative tensor CP decomposition of hyperspectral data,” IEEE Trans. Geosci. Remote Sens. 54, 2577–2588 (2016).
[Crossref]

D. Zhao, S. Zhu, and F. Wang, “Lossy hyperspectral image compression based on intra-band prediction and inter-band fractal encoding,” Comput. Electr. Eng. 54, 494–505 (2016).
[Crossref]

Y. Nian, Y. Liu, and Z. Ye, “Pairwise KLT-based compression for multispectral images,” Sens. Imaging 17, 1–15 (2016).
[Crossref]

2015 (4)

S. Zhu, D. Zhao, and F. Wang, “Hybrid prediction and fractal hyperspectral image compression,” Math. Probl. Eng. 2015, 950357 (2015).
[Crossref]

L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, “Compression of hyperspectral remote sensing images by tensor approach,” Neurocomputing 147, 358–363 (2015).
[Crossref]

C. Wang, M. Gong, M. Zhang, and Y. Chan, “Unsupervised hyperspectral image band selection via column subset selection,” IEEE Geosci. Remote Sens. Lett. 12, 1411–1415 (2015).

I. Ülkü and B. U. Töreyin, “Sparse coding of hyperspectral imagery using online learning,” Signal Image Video Process. 9, 959–966 (2015).
[Crossref]

2014 (4)

F. Gao, X. Ji, C. Yan, and Q. Dai, “Compression of multispectral image using HEVC,” Proc. SPIE 9273, 92732X (2014).
[Crossref]

G. G. King, C. C. Seldev, and N. A. Singh, “A novel compression technique for compound images using parallel Lempel–Ziv–Welch algorithm,” Appl. Mech. Mater. 626, 44–51 (2014).
[Crossref]

T. Qiao, J. Ren, M. Sun, J. Zheng, and S. Marshall, “Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications,” Int. J. Remote Sens. 35, 7316–7337 (2014).
[Crossref]

X. Li, J. Ren, C. Zhao, T. Qiao, and S. Marshall, “Novel multivariate vector quantization for effective compression of hyperspectral imagery,” Opt. Commun. 332, 192–200 (2014).
[Crossref]

2013 (4)

M. Zala and S. Parmar, “3D Wavelet transform with SPIHT algorithm for image compression,” Int. J. Appl. Innov. Eng. Manage. 2, 384–392 (2013).

Y. Qian, M. Ye, and J. Zhou, “Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features,” IEEE Trans. Geosci. Remote Sens. 51, 2276–2291 (2013).
[Crossref]

Y. Qian and M. Ye, “Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 6, 499–515 (2013).
[Crossref]

N. R. Mat Noor and T. Vladimirova, “Investigation into lossless hyperspectral image compression for satellite remote sensing,” Int. J. Remote Sens. 34, 5072–5104 (2013).
[Crossref]

2012 (6)

J. Mielikainen and B. Huang, “Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length,” IEEE Geosci. Remote Sens. Lett. 9, 1118–1121 (2012).
[Crossref]

X. Pan, R. Liu, and X. Lv, “Low-complexity compression method for hyperspectral images based on distributed source coding,” IEEE Geosci. Remote Sens. Lett. 9, 224–227 (2012).
[Crossref]

H. H. Zayed, S. E. Kishk, and H. M. Ahmed, “3D wavelets with SPIHT coding for integral imaging compression,” Int. J. Comput. Sci. Netw. Secur. 12, 126–133 (2012).

J. Zhang, Y. Li, K. Wang, and H. Liu, “The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate,” Proc. SPIE 8514, 85140W (2012).
[Crossref]

A. Karami, M. Yazdi, and G. Mercier, “Compression of hyperspectral images using discerete wavelet transform and Tucker decomposition,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 444–450 (2012).
[Crossref]

L. Santos, S. Lopez, G. M. Callico, J. F. Lopez, and R. Sarmiento, “Performance evaluation of the H. 264/AVC video coding standard for lossy hyperspectral image compression,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 451–461 (2012).
[Crossref]

2011 (4)

A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro, “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 49, 4263–4281 (2011).
[Crossref]

C.-C. Lin and Y.-T. Hwang, “Lossless compression of hyperspectral images using adaptive prediction and backward search schemes,” J. Inf. Sci. Eng. 27, 419–435 (2011).

S. Raja and A. Suruliandi, “Image compression using WDR & ASWDR techniques with different wavelet codecs,” ACEEE Int. J. Inf. Technol. 1, 23–26 (2011).
[Crossref]

A. S. Charles, B. A. Olshausen, and C. J. Rozell, “Learning sparse codes for hyperspectral imagery,” IEEE J. Sel. Top. Signal Process. 5, 963–978 (2011).
[Crossref]

2010 (5)

J. Gonzalez-Conejero, J. Bartrina-Rapesta, and J. Serra-Sagrista, “JPEG2000 encoding of remote sensing multispectral images with no-data regions,” IEEE Geosci. Remote Sens. Lett. 7, 251–255 (2010).
[Crossref]

V. Shingate, T. Sontakke, and S. Talbar, “Still image compression using embedded zerotree wavelet encoding,” Int. J. Comput. Sci. Commun. 1, 21–24 (2010).

I. Blanes and J. Serra-Sagristà, “Cost and scalability improvements to the Karhunen–Loêve transform for remote-sensing image coding,” IEEE Trans. Geosci. Remote Sens. 48, 2854–2863 (2010).
[Crossref]

C.-C. Lin and Y.-T. Hwang, “An efficient lossless compression scheme for hyperspectral images using two-stage prediction,” IEEE Geosci. Remote Sens. Lett. 7, 558–562 (2010).
[Crossref]

F. Zhao, G. Liu, and X. Wang, “An efficient macroblock-based diverse and flexible prediction modes selection for hyperspectral images coding,” Signal Process. 25, 697–708 (2010).
[Crossref]

2009 (1)

L. Wang, J. Wu, L. Jiao, and G. Shi, “Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT,” IEEE Geosci. Remote Sens. Lett. 6, 587–591 (2009).
[Crossref]

2007 (4)

B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
[Crossref]

Q. Du and J. E. Fowler, “Hyperspectral image compression using JPEG2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4, 201–205 (2007).
[Crossref]

G. Dudek, P. Borys, and Z. J. Grzywna, “Lossy dictionary-based image compression method,” Image Vision Comput. 25, 883–889 (2007).
[Crossref]

M. F. M. Salleh and J. Soraghan, “A new multistage lattice vector quantization with adaptive subband thresholding for image compression,” EURASIP J. Adv. Signal Process. 2007, 092928 (2007).
[Crossref]

2006 (2)

D. H. Foster, K. Amano, S. M. Nascimento, and M. J. Foster, “Frequency of metamerism in natural scenes,” J. Opt. Soc. Am. A 23, 2359–2372 (2006).
[Crossref]

J. Wu, Z. Wu, and C. Wu, “Lossy to lossless compressions of hyperspectral images using three-dimensional set partitioning algorithm,” Opt. Eng. 45, 027005 (2006).
[Crossref]

2004 (1)

E. Magli, G. Olmo, and E. Quacchio, “Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC,” IEEE Geosci. Remote Sens. Lett. 1, 21–25 (2004).
[Crossref]

2003 (1)

X. Tang, W. A. Pearlman, and J. W. Modestino, “Hyperspectral image compression using three-dimensional wavelet coding,” Proc. SPIE 5022, 1037–1047 (2003).
[Crossref]

2001 (1)

A. Skodras, C. Christopoulos, and T. Ebrahimi, “The JPEG 2000 still image compression standard,” IEEE Signal Process. Mag. 18(5), 36–58 (2001).
[Crossref]

2000 (1)

X. Wu and N. Memon, “Context-based lossless interband compression-extending CALIC,” IEEE Trans. Image Process. 9, 994–1001 (2000).
[Crossref]

1994 (1)

W. F. Good, G. S. Maitz, and D. Gur, “Joint Photographic Experts Group (JPEG) compatible data compression of mammograms,” J. Digit. Imaging 7, 123–132 (1994).

1970 (1)

J. D. Carroll and J.-J. Chang, “Analysis of individual differences in multidimensional scaling via an N-way generalization of ‘Eckart–Young’ decomposition,” Psychometrika 35, 283–319 (1970).
[Crossref]

1966 (1)

L. R. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika 31, 279–311 (1966).
[Crossref]

1927 (1)

F. L. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,” Stud. Appl. Math. 6, 164–189 (1927).
[Crossref]

Ahmed, H. M.

H. H. Zayed, S. E. Kishk, and H. M. Ahmed, “3D wavelets with SPIHT coding for integral imaging compression,” Int. J. Comput. Sci. Netw. Secur. 12, 126–133 (2012).

Ali, M.

S. Shahriyar, M. Paul, M. Murshed, and M. Ali, “Lossless hyperspectral image compression using binary tree based decomposition,” in International Conference on Digital Image Computing: Techniques and Applications (DICTA) (IEEE, 2016), pp. 1–8.

Amano, K.

Amrani, N.

N. Amrani, J. Serra-Sagristà, V. Laparra, M. W. Marcellin, and J. Malo, “Regression wavelet analysis for lossless coding of remote-sensing data,” IEEE Trans. Geosci. Remote Sens. 54, 5616–5627 (2016).
[Crossref]

Bartrina-Rapesta, J.

J. Gonzalez-Conejero, J. Bartrina-Rapesta, and J. Serra-Sagrista, “JPEG2000 encoding of remote sensing multispectral images with no-data regions,” IEEE Geosci. Remote Sens. Lett. 7, 251–255 (2010).
[Crossref]

Benediktsson, J. A.

B. Rasti, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, “Hyperspectral image denoising using 3D wavelets,” in IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1349–1352.

Blanes, I.

I. Blanes and J. Serra-Sagristà, “Cost and scalability improvements to the Karhunen–Loêve transform for remote-sensing image coding,” IEEE Trans. Geosci. Remote Sens. 48, 2854–2863 (2010).
[Crossref]

Borys, P.

G. Dudek, P. Borys, and Z. J. Grzywna, “Lossy dictionary-based image compression method,” Image Vision Comput. 25, 883–889 (2007).
[Crossref]

Bosch, E.

A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro, “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 49, 4263–4281 (2011).
[Crossref]

Bossomaier, T.

M. Paul, R. Xiao, J. Gao, and T. Bossomaier, “Reflectance prediction modelling for residual-based hyperspectral image coding,” PloS one 11, e0161212 (2016).
[Crossref]

R. Dusselaar, M. Paul, and T. Bossomaier, “Hyperspectral image coding using spectral prediction modelling in HEVC coding framework,” in International Conference on Image and Vision Computing New Zealand (IVCNZ) (IEEE, 2015), pp. 1–6.

Bovik, A. C.

A. C. Bovik, Handbook of Image and Video Processing (Academic, 2010).

Callico, G. M.

L. Santos, S. Lopez, G. M. Callico, J. F. Lopez, and R. Sarmiento, “Performance evaluation of the H. 264/AVC video coding standard for lossy hyperspectral image compression,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 451–461 (2012).
[Crossref]

Camps-Valls, G.

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. Remote Sens. 54, 1349–1362 (2016).
[Crossref]

Carin, L.

A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro, “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 49, 4263–4281 (2011).
[Crossref]

Carroll, J. D.

J. D. Carroll and J.-J. Chang, “Analysis of individual differences in multidimensional scaling via an N-way generalization of ‘Eckart–Young’ decomposition,” Psychometrika 35, 283–319 (1970).
[Crossref]

Castrodad, A.

A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro, “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 49, 4263–4281 (2011).
[Crossref]

Chan, Y.

C. Wang, M. Gong, M. Zhang, and Y. Chan, “Unsupervised hyperspectral image band selection via column subset selection,” IEEE Geosci. Remote Sens. Lett. 12, 1411–1415 (2015).

Chang, C.-I.

C.-I. Chang, Hyperspectral Data Processing: Algorithm Design and Analysis (Wiley, 2013).

Chang, J.-J.

J. D. Carroll and J.-J. Chang, “Analysis of individual differences in multidimensional scaling via an N-way generalization of ‘Eckart–Young’ decomposition,” Psychometrika 35, 283–319 (1970).
[Crossref]

Chanussot, J.

M. A. Veganzones, J. E. Cohen, R. C. Farias, J. Chanussot, and P. Comon, “Nonnegative tensor CP decomposition of hyperspectral data,” IEEE Trans. Geosci. Remote Sens. 54, 2577–2588 (2016).
[Crossref]

Charles, A. S.

A. S. Charles, B. A. Olshausen, and C. J. Rozell, “Learning sparse codes for hyperspectral imagery,” IEEE J. Sel. Top. Signal Process. 5, 963–978 (2011).
[Crossref]

Chen, Y.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54, 6232–6251 (2016).
[Crossref]

Cho, S.

X. Tang, S. Cho, and W. A. Pearlman, “3D set partitioning coding methods in hyperspectral image compression,” in International Conference on Image Processing ICIP (IEEE, 2003), p. II-239.

Christopoulos, C.

A. Skodras, C. Christopoulos, and T. Ebrahimi, “The JPEG 2000 still image compression standard,” IEEE Signal Process. Mag. 18(5), 36–58 (2001).
[Crossref]

Clinton, N.

W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
[Crossref]

Cohen, J. E.

M. A. Veganzones, J. E. Cohen, R. C. Farias, J. Chanussot, and P. Comon, “Nonnegative tensor CP decomposition of hyperspectral data,” IEEE Trans. Geosci. Remote Sens. 54, 2577–2588 (2016).
[Crossref]

Comon, P.

M. A. Veganzones, J. E. Cohen, R. C. Farias, J. Chanussot, and P. Comon, “Nonnegative tensor CP decomposition of hyperspectral data,” IEEE Trans. Geosci. Remote Sens. 54, 2577–2588 (2016).
[Crossref]

Covell, M.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” arXiv:1608.05148 (2016).

Dai, Q.

F. Gao, X. Ji, C. Yan, and Q. Dai, “Compression of multispectral image using HEVC,” Proc. SPIE 9273, 92732X (2014).
[Crossref]

Datcu, M.

D. Marmanis, M. Datcu, T. Esch, and U. Stilla, “Deep learning earth observation classification using ImageNet pretrained networks,” IEEE Geosci. Remote Sens. Lett. 13, 105–109 (2016).
[Crossref]

Du, B.

L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, “Compression of hyperspectral remote sensing images by tensor approach,” Neurocomputing 147, 358–363 (2015).
[Crossref]

Du, Q.

Q. Du and J. E. Fowler, “Hyperspectral image compression using JPEG2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4, 201–205 (2007).
[Crossref]

Q. Du, N. Ly, and J. E. Fowler, “An operational approach for hyperspectral image compression,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1357–1360.

Du, S.

W. Zhao and S. Du, “Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach,” IEEE Trans. Geosci. Remote Sens. 54, 4544–4554 (2016).
[Crossref]

Dudek, G.

G. Dudek, P. Borys, and Z. J. Grzywna, “Lossy dictionary-based image compression method,” Image Vision Comput. 25, 883–889 (2007).
[Crossref]

Dusselaar, R.

R. Dusselaar, M. Paul, and T. Bossomaier, “Hyperspectral image coding using spectral prediction modelling in HEVC coding framework,” in International Conference on Image and Vision Computing New Zealand (IVCNZ) (IEEE, 2015), pp. 1–6.

Ebrahimi, T.

A. Skodras, C. Christopoulos, and T. Ebrahimi, “The JPEG 2000 still image compression standard,” IEEE Signal Process. Mag. 18(5), 36–58 (2001).
[Crossref]

Esch, T.

D. Marmanis, M. Datcu, T. Esch, and U. Stilla, “Deep learning earth observation classification using ImageNet pretrained networks,” IEEE Geosci. Remote Sens. Lett. 13, 105–109 (2016).
[Crossref]

Farias, R. C.

M. A. Veganzones, J. E. Cohen, R. C. Farias, J. Chanussot, and P. Comon, “Nonnegative tensor CP decomposition of hyperspectral data,” IEEE Trans. Geosci. Remote Sens. 54, 2577–2588 (2016).
[Crossref]

Feng, D.

W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
[Crossref]

Foster, D. H.

Foster, M. J.

Fowler, J. E.

Q. Du and J. E. Fowler, “Hyperspectral image compression using JPEG2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4, 201–205 (2007).
[Crossref]

Q. Du, N. Ly, and J. E. Fowler, “An operational approach for hyperspectral image compression,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1357–1360.

Fu, H.

W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
[Crossref]

Gao, F.

F. Gao, X. Ji, C. Yan, and Q. Dai, “Compression of multispectral image using HEVC,” Proc. SPIE 9273, 92732X (2014).
[Crossref]

Gao, J.

M. Paul, R. Xiao, J. Gao, and T. Bossomaier, “Reflectance prediction modelling for residual-based hyperspectral image coding,” PloS one 11, e0161212 (2016).
[Crossref]

Gatta, C.

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. Remote Sens. 54, 1349–1362 (2016).
[Crossref]

George, R.

R. George and M. Manimekalai, “A novel approach for image compression using zero tree coding,” in International Conference on Electronics and Communication Systems (ICECS) (IEEE, 2014), pp. 1–5.

Ghamisi, P.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54, 6232–6251 (2016).
[Crossref]

Gong, M.

C. Wang, M. Gong, M. Zhang, and Y. Chan, “Unsupervised hyperspectral image band selection via column subset selection,” IEEE Geosci. Remote Sens. Lett. 12, 1411–1415 (2015).

Gong, P.

W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
[Crossref]

Gonzalez-Conejero, J.

J. Gonzalez-Conejero, J. Bartrina-Rapesta, and J. Serra-Sagrista, “JPEG2000 encoding of remote sensing multispectral images with no-data regions,” IEEE Geosci. Remote Sens. Lett. 7, 251–255 (2010).
[Crossref]

Good, W. F.

W. F. Good, G. S. Maitz, and D. Gur, “Joint Photographic Experts Group (JPEG) compatible data compression of mammograms,” J. Digit. Imaging 7, 123–132 (1994).

Greer, J. B.

A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro, “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 49, 4263–4281 (2011).
[Crossref]

Grigorev, A.

W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).

Grzywna, Z. J.

G. Dudek, P. Borys, and Z. J. Grzywna, “Lossy dictionary-based image compression method,” Image Vision Comput. 25, 883–889 (2007).
[Crossref]

Gur, D.

W. F. Good, G. S. Maitz, and D. Gur, “Joint Photographic Experts Group (JPEG) compatible data compression of mammograms,” J. Digit. Imaging 7, 123–132 (1994).

Harshman, R. A.

R. A. Harshman, “Foundations of the PARAFAC procedure: models and conditions for an ‘explanatory’ multi-modal factor analysis,” in UCLA Working Papers in Phonetics (1970), Vol. 16, pp. 1–84.

Heylen, R.

A. Karami, R. Heylen, and P. Scheunders, “Hyperspectral image compression optimized for spectral unmixing,” IEEE Trans. Geosci. Remote Sens. 54, 5884–5894 (2016).
[Crossref]

Hitchcock, F. L.

F. L. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,” Stud. Appl. Math. 6, 164–189 (1927).
[Crossref]

Huang, B.

J. Mielikainen and B. Huang, “Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length,” IEEE Geosci. Remote Sens. Lett. 9, 1118–1121 (2012).
[Crossref]

Huang, X.

L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, “Compression of hyperspectral remote sensing images by tensor approach,” Neurocomputing 147, 358–363 (2015).
[Crossref]

Hwang, S. J.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” arXiv:1608.05148 (2016).

Hwang, Y.-T.

C.-C. Lin and Y.-T. Hwang, “Lossless compression of hyperspectral images using adaptive prediction and backward search schemes,” J. Inf. Sci. Eng. 27, 419–435 (2011).

C.-C. Lin and Y.-T. Hwang, “An efficient lossless compression scheme for hyperspectral images using two-stage prediction,” IEEE Geosci. Remote Sens. Lett. 7, 558–562 (2010).
[Crossref]

Ji, X.

F. Gao, X. Ji, C. Yan, and Q. Dai, “Compression of multispectral image using HEVC,” Proc. SPIE 9273, 92732X (2014).
[Crossref]

Jia, X.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54, 6232–6251 (2016).
[Crossref]

Jiang, F.

W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).

Jiang, H.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54, 6232–6251 (2016).
[Crossref]

Jiao, L.

L. Wang, J. Wu, L. Jiao, and G. Shi, “Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT,” IEEE Geosci. Remote Sens. Lett. 6, 587–591 (2009).
[Crossref]

Jifara, W.

W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).

Jing, Y. F.

X. J. Zhao and Y. F. Jing, “The application of vector quantization algorithm in hyperspectral image compression,” in Advanced Materials Research (Trans Tech Publication, 2013), pp. 1479–1483.

Johnston, N.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” arXiv:1608.05148 (2016).

Karami, A.

A. Karami, R. Heylen, and P. Scheunders, “Hyperspectral image compression optimized for spectral unmixing,” IEEE Trans. Geosci. Remote Sens. 54, 5884–5894 (2016).
[Crossref]

A. Karami, M. Yazdi, and G. Mercier, “Compression of hyperspectral images using discerete wavelet transform and Tucker decomposition,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 444–450 (2012).
[Crossref]

King, G. G.

G. G. King, C. C. Seldev, and N. A. Singh, “A novel compression technique for compound images using parallel Lempel–Ziv–Welch algorithm,” Appl. Mech. Mater. 626, 44–51 (2014).
[Crossref]

Kishk, S. E.

H. H. Zayed, S. E. Kishk, and H. M. Ahmed, “3D wavelets with SPIHT coding for integral imaging compression,” Int. J. Comput. Sci. Netw. Secur. 12, 126–133 (2012).

Kussul, N.

N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep learning classification of land cover and crop types using remote sensing data,” IEEE Geosci. Remote Sens. Lett. 14, 778–782 (2017).
[Crossref]

Laparra, V.

N. Amrani, J. Serra-Sagristà, V. Laparra, M. W. Marcellin, and J. Malo, “Regression wavelet analysis for lossless coding of remote-sensing data,” IEEE Trans. Geosci. Remote Sens. 54, 5616–5627 (2016).
[Crossref]

Lavreniuk, M.

N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep learning classification of land cover and crop types using remote sensing data,” IEEE Geosci. Remote Sens. Lett. 14, 778–782 (2017).
[Crossref]

Li, C.

W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
[Crossref]

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54, 6232–6251 (2016).
[Crossref]

Li, J.

W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).

Li, W.

W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
[Crossref]

Li, X.

X. Li, J. Ren, C. Zhao, T. Qiao, and S. Marshall, “Novel multivariate vector quantization for effective compression of hyperspectral imagery,” Opt. Commun. 332, 192–200 (2014).
[Crossref]

Li, Y.

J. Zhang, Y. Li, K. Wang, and H. Liu, “The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate,” Proc. SPIE 8514, 85140W (2012).
[Crossref]

Lin, C.-C.

C.-C. Lin and Y.-T. Hwang, “Lossless compression of hyperspectral images using adaptive prediction and backward search schemes,” J. Inf. Sci. Eng. 27, 419–435 (2011).

C.-C. Lin and Y.-T. Hwang, “An efficient lossless compression scheme for hyperspectral images using two-stage prediction,” IEEE Geosci. Remote Sens. Lett. 7, 558–562 (2010).
[Crossref]

Liu, G.

F. Zhao, G. Liu, and X. Wang, “An efficient macroblock-based diverse and flexible prediction modes selection for hyperspectral images coding,” Signal Process. 25, 697–708 (2010).
[Crossref]

Liu, H.

J. Zhang, Y. Li, K. Wang, and H. Liu, “The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate,” Proc. SPIE 8514, 85140W (2012).
[Crossref]

Liu, R.

X. Pan, R. Liu, and X. Lv, “Low-complexity compression method for hyperspectral images based on distributed source coding,” IEEE Geosci. Remote Sens. Lett. 9, 224–227 (2012).
[Crossref]

Liu, S.

W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).

Liu, Y.

Y. Nian, Y. Liu, and Z. Ye, “Pairwise KLT-based compression for multispectral images,” Sens. Imaging 17, 1–15 (2016).
[Crossref]

Lopez, J. F.

L. Santos, S. Lopez, G. M. Callico, J. F. Lopez, and R. Sarmiento, “Performance evaluation of the H. 264/AVC video coding standard for lossy hyperspectral image compression,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 451–461 (2012).
[Crossref]

Lopez, S.

L. Santos, S. Lopez, G. M. Callico, J. F. Lopez, and R. Sarmiento, “Performance evaluation of the H. 264/AVC video coding standard for lossy hyperspectral image compression,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 451–461 (2012).
[Crossref]

Lv, X.

X. Pan, R. Liu, and X. Lv, “Low-complexity compression method for hyperspectral images based on distributed source coding,” IEEE Geosci. Remote Sens. Lett. 9, 224–227 (2012).
[Crossref]

Ly, N.

Q. Du, N. Ly, and J. E. Fowler, “An operational approach for hyperspectral image compression,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1357–1360.

Magli, E.

B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
[Crossref]

E. Magli, G. Olmo, and E. Quacchio, “Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC,” IEEE Geosci. Remote Sens. Lett. 1, 21–25 (2004).
[Crossref]

Maitz, G. S.

W. F. Good, G. S. Maitz, and D. Gur, “Joint Photographic Experts Group (JPEG) compatible data compression of mammograms,” J. Digit. Imaging 7, 123–132 (1994).

Malo, J.

N. Amrani, J. Serra-Sagristà, V. Laparra, M. W. Marcellin, and J. Malo, “Regression wavelet analysis for lossless coding of remote-sensing data,” IEEE Trans. Geosci. Remote Sens. 54, 5616–5627 (2016).
[Crossref]

Manimekalai, M.

R. George and M. Manimekalai, “A novel approach for image compression using zero tree coding,” in International Conference on Electronics and Communication Systems (ICECS) (IEEE, 2014), pp. 1–5.

Marcellin, M. W.

N. Amrani, J. Serra-Sagristà, V. Laparra, M. W. Marcellin, and J. Malo, “Regression wavelet analysis for lossless coding of remote-sensing data,” IEEE Trans. Geosci. Remote Sens. 54, 5616–5627 (2016).
[Crossref]

Marmanis, D.

D. Marmanis, M. Datcu, T. Esch, and U. Stilla, “Deep learning earth observation classification using ImageNet pretrained networks,” IEEE Geosci. Remote Sens. Lett. 13, 105–109 (2016).
[Crossref]

Marshall, S.

X. Li, J. Ren, C. Zhao, T. Qiao, and S. Marshall, “Novel multivariate vector quantization for effective compression of hyperspectral imagery,” Opt. Commun. 332, 192–200 (2014).
[Crossref]

T. Qiao, J. Ren, M. Sun, J. Zheng, and S. Marshall, “Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications,” Int. J. Remote Sens. 35, 7316–7337 (2014).
[Crossref]

Mat Noor, N. R.

N. R. Mat Noor and T. Vladimirova, “Investigation into lossless hyperspectral image compression for satellite remote sensing,” Int. J. Remote Sens. 34, 5072–5104 (2013).
[Crossref]

Memon, N.

X. Wu and N. Memon, “Context-based lossless interband compression-extending CALIC,” IEEE Trans. Image Process. 9, 994–1001 (2000).
[Crossref]

Mercier, G.

A. Karami, M. Yazdi, and G. Mercier, “Compression of hyperspectral images using discerete wavelet transform and Tucker decomposition,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 444–450 (2012).
[Crossref]

Mielikainen, J.

J. Mielikainen and B. Huang, “Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length,” IEEE Geosci. Remote Sens. Lett. 9, 1118–1121 (2012).
[Crossref]

Minnen, D.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” arXiv:1608.05148 (2016).

Modestino, J. W.

X. Tang, W. A. Pearlman, and J. W. Modestino, “Hyperspectral image compression using three-dimensional wavelet coding,” Proc. SPIE 5022, 1037–1047 (2003).
[Crossref]

Motta, G.

G. Motta, F. Rizzo, and J. A. Storer, Hyperspectral Data Compression (Springer, 2006).

Murshed, M.

S. Shahriyar, M. Paul, M. Murshed, and M. Ali, “Lossless hyperspectral image compression using binary tree based decomposition,” in International Conference on Digital Image Computing: Techniques and Applications (DICTA) (IEEE, 2016), pp. 1–8.

P. K. Podder, M. Paul, and M. Murshed, “Efficient coding strategy for HEVC performance improvement by exploiting motion features,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2015), pp. 1414–1418.

Nascimento, S. M.

Nian, Y.

Y. Nian, Y. Liu, and Z. Ye, “Pairwise KLT-based compression for multispectral images,” Sens. Imaging 17, 1–15 (2016).
[Crossref]

Olmo, G.

B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
[Crossref]

E. Magli, G. Olmo, and E. Quacchio, “Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC,” IEEE Geosci. Remote Sens. Lett. 1, 21–25 (2004).
[Crossref]

Olshausen, B. A.

A. S. Charles, B. A. Olshausen, and C. J. Rozell, “Learning sparse codes for hyperspectral imagery,” IEEE J. Sel. Top. Signal Process. 5, 963–978 (2011).
[Crossref]

Pabich, P. J.

P. J. Pabich, Hyperspectral Imagery: Warfighting Through a Different Set of Eyes (Defense Technical Information Center, 2002).

Pan, W. D.

H. Shen, W. D. Pan, and D. Wu, “Predictive lossless compression of regions of interest in hyperspectral images with no-data regions,” IEEE Trans. Geosci. Remote Sens. 55, 173–182 (2017).
[Crossref]

Pan, X.

X. Pan, R. Liu, and X. Lv, “Low-complexity compression method for hyperspectral images based on distributed source coding,” IEEE Geosci. Remote Sens. Lett. 9, 224–227 (2012).
[Crossref]

Parmar, S.

M. Zala and S. Parmar, “3D Wavelet transform with SPIHT algorithm for image compression,” Int. J. Appl. Innov. Eng. Manage. 2, 384–392 (2013).

Paul, M.

M. Paul, R. Xiao, J. Gao, and T. Bossomaier, “Reflectance prediction modelling for residual-based hyperspectral image coding,” PloS one 11, e0161212 (2016).
[Crossref]

R. Dusselaar, M. Paul, and T. Bossomaier, “Hyperspectral image coding using spectral prediction modelling in HEVC coding framework,” in International Conference on Image and Vision Computing New Zealand (IVCNZ) (IEEE, 2015), pp. 1–6.

P. K. Podder, M. Paul, and M. Murshed, “Efficient coding strategy for HEVC performance improvement by exploiting motion features,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2015), pp. 1414–1418.

S. Shahriyar, M. Paul, M. Murshed, and M. Ali, “Lossless hyperspectral image compression using binary tree based decomposition,” in International Conference on Digital Image Computing: Techniques and Applications (DICTA) (IEEE, 2016), pp. 1–8.

Pearlman, W. A.

X. Tang, W. A. Pearlman, and J. W. Modestino, “Hyperspectral image compression using three-dimensional wavelet coding,” Proc. SPIE 5022, 1037–1047 (2003).
[Crossref]

X. Tang, S. Cho, and W. A. Pearlman, “3D set partitioning coding methods in hyperspectral image compression,” in International Conference on Image Processing ICIP (IEEE, 2003), p. II-239.

X. Tang and W. A. Pearlman, “Three-dimensional wavelet-based compression of hyperspectral images,” in Hyperspectral Data Compression (Springer, 2006), pp. 273–308.

Penna, B.

B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
[Crossref]

Podder, P. K.

P. K. Podder, M. Paul, and M. Murshed, “Efficient coding strategy for HEVC performance improvement by exploiting motion features,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2015), pp. 1414–1418.

Prasad, M.

S. Shukla and M. Prasad, Lossy Image Compression: Domain Decomposition-Based Algorithms (Springer, 2011).

Qian, Y.

Y. Qian and M. Ye, “Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 6, 499–515 (2013).
[Crossref]

Y. Qian, M. Ye, and J. Zhou, “Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features,” IEEE Trans. Geosci. Remote Sens. 51, 2276–2291 (2013).
[Crossref]

Qiao, T.

X. Li, J. Ren, C. Zhao, T. Qiao, and S. Marshall, “Novel multivariate vector quantization for effective compression of hyperspectral imagery,” Opt. Commun. 332, 192–200 (2014).
[Crossref]

T. Qiao, J. Ren, M. Sun, J. Zheng, and S. Marshall, “Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications,” Int. J. Remote Sens. 35, 7316–7337 (2014).
[Crossref]

Quacchio, E.

E. Magli, G. Olmo, and E. Quacchio, “Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC,” IEEE Geosci. Remote Sens. Lett. 1, 21–25 (2004).
[Crossref]

Raja, S.

S. Raja and A. Suruliandi, “Image compression using WDR & ASWDR techniques with different wavelet codecs,” ACEEE Int. J. Inf. Technol. 1, 23–26 (2011).
[Crossref]

Rasti, B.

B. Rasti, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, “Hyperspectral image denoising using 3D wavelets,” in IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1349–1352.

Ren, J.

X. Li, J. Ren, C. Zhao, T. Qiao, and S. Marshall, “Novel multivariate vector quantization for effective compression of hyperspectral imagery,” Opt. Commun. 332, 192–200 (2014).
[Crossref]

T. Qiao, J. Ren, M. Sun, J. Zheng, and S. Marshall, “Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications,” Int. J. Remote Sens. 35, 7316–7337 (2014).
[Crossref]

Rizzo, F.

G. Motta, F. Rizzo, and J. A. Storer, Hyperspectral Data Compression (Springer, 2006).

Romero, A.

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. Remote Sens. 54, 1349–1362 (2016).
[Crossref]

Rozell, C. J.

A. S. Charles, B. A. Olshausen, and C. J. Rozell, “Learning sparse codes for hyperspectral imagery,” IEEE J. Sel. Top. Signal Process. 5, 963–978 (2011).
[Crossref]

Salleh, M. F. M.

M. F. M. Salleh and J. Soraghan, “A new multistage lattice vector quantization with adaptive subband thresholding for image compression,” EURASIP J. Adv. Signal Process. 2007, 092928 (2007).
[Crossref]

Santos, L.

L. Santos, S. Lopez, G. M. Callico, J. F. Lopez, and R. Sarmiento, “Performance evaluation of the H. 264/AVC video coding standard for lossy hyperspectral image compression,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 451–461 (2012).
[Crossref]

Sapiro, G.

A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro, “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 49, 4263–4281 (2011).
[Crossref]

Sarmiento, R.

L. Santos, S. Lopez, G. M. Callico, J. F. Lopez, and R. Sarmiento, “Performance evaluation of the H. 264/AVC video coding standard for lossy hyperspectral image compression,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 451–461 (2012).
[Crossref]

Sayood, K.

K. Sayood, Introduction to Data Compression (Newnes, 2012).

Scheunders, P.

A. Karami, R. Heylen, and P. Scheunders, “Hyperspectral image compression optimized for spectral unmixing,” IEEE Trans. Geosci. Remote Sens. 54, 5884–5894 (2016).
[Crossref]

Seldev, C. C.

G. G. King, C. C. Seldev, and N. A. Singh, “A novel compression technique for compound images using parallel Lempel–Ziv–Welch algorithm,” Appl. Mech. Mater. 626, 44–51 (2014).
[Crossref]

Serra-Sagrista, J.

J. Gonzalez-Conejero, J. Bartrina-Rapesta, and J. Serra-Sagrista, “JPEG2000 encoding of remote sensing multispectral images with no-data regions,” IEEE Geosci. Remote Sens. Lett. 7, 251–255 (2010).
[Crossref]

Serra-Sagristà, J.

N. Amrani, J. Serra-Sagristà, V. Laparra, M. W. Marcellin, and J. Malo, “Regression wavelet analysis for lossless coding of remote-sensing data,” IEEE Trans. Geosci. Remote Sens. 54, 5616–5627 (2016).
[Crossref]

I. Blanes and J. Serra-Sagristà, “Cost and scalability improvements to the Karhunen–Loêve transform for remote-sensing image coding,” IEEE Trans. Geosci. Remote Sens. 48, 2854–2863 (2010).
[Crossref]

Shahriyar, S.

S. Shahriyar, M. Paul, M. Murshed, and M. Ali, “Lossless hyperspectral image compression using binary tree based decomposition,” in International Conference on Digital Image Computing: Techniques and Applications (DICTA) (IEEE, 2016), pp. 1–8.

Shelestov, A.

N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep learning classification of land cover and crop types using remote sensing data,” IEEE Geosci. Remote Sens. Lett. 14, 778–782 (2017).
[Crossref]

Shen, H.

H. Shen, W. D. Pan, and D. Wu, “Predictive lossless compression of regions of interest in hyperspectral images with no-data regions,” IEEE Trans. Geosci. Remote Sens. 55, 173–182 (2017).
[Crossref]

Shi, G.

L. Wang, J. Wu, L. Jiao, and G. Shi, “Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT,” IEEE Geosci. Remote Sens. Lett. 6, 587–591 (2009).
[Crossref]

Shingate, V.

V. Shingate, T. Sontakke, and S. Talbar, “Still image compression using embedded zerotree wavelet encoding,” Int. J. Comput. Sci. Commun. 1, 21–24 (2010).

Shor, J.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” arXiv:1608.05148 (2016).

Shukla, S.

S. Shukla and M. Prasad, Lossy Image Compression: Domain Decomposition-Based Algorithms (Springer, 2011).

Singh, N. A.

G. G. King, C. C. Seldev, and N. A. Singh, “A novel compression technique for compound images using parallel Lempel–Ziv–Welch algorithm,” Appl. Mech. Mater. 626, 44–51 (2014).
[Crossref]

Skakun, S.

N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep learning classification of land cover and crop types using remote sensing data,” IEEE Geosci. Remote Sens. Lett. 14, 778–782 (2017).
[Crossref]

Skodras, A.

A. Skodras, C. Christopoulos, and T. Ebrahimi, “The JPEG 2000 still image compression standard,” IEEE Signal Process. Mag. 18(5), 36–58 (2001).
[Crossref]

Sontakke, T.

V. Shingate, T. Sontakke, and S. Talbar, “Still image compression using embedded zerotree wavelet encoding,” Int. J. Comput. Sci. Commun. 1, 21–24 (2010).

Soraghan, J.

M. F. M. Salleh and J. Soraghan, “A new multistage lattice vector quantization with adaptive subband thresholding for image compression,” EURASIP J. Adv. Signal Process. 2007, 092928 (2007).
[Crossref]

Stasinski, R.

G. Ulacha and R. Stasiński, “New context-based adaptive linear prediction algorithm for lossless image coding,” in International Conference on Signals and Electronic Systems (ICSES) (IEEE, 2014), pp. 1–4.

Stilla, U.

D. Marmanis, M. Datcu, T. Esch, and U. Stilla, “Deep learning earth observation classification using ImageNet pretrained networks,” IEEE Geosci. Remote Sens. Lett. 13, 105–109 (2016).
[Crossref]

Storer, J. A.

G. Motta, F. Rizzo, and J. A. Storer, Hyperspectral Data Compression (Springer, 2006).

Sun, M.

T. Qiao, J. Ren, M. Sun, J. Zheng, and S. Marshall, “Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications,” Int. J. Remote Sens. 35, 7316–7337 (2014).
[Crossref]

Suruliandi, A.

S. Raja and A. Suruliandi, “Image compression using WDR & ASWDR techniques with different wavelet codecs,” ACEEE Int. J. Inf. Technol. 1, 23–26 (2011).
[Crossref]

Sveinsson, J. R.

B. Rasti, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, “Hyperspectral image denoising using 3D wavelets,” in IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1349–1352.

Talbar, S.

V. Shingate, T. Sontakke, and S. Talbar, “Still image compression using embedded zerotree wavelet encoding,” Int. J. Comput. Sci. Commun. 1, 21–24 (2010).

Tang, X.

X. Tang, W. A. Pearlman, and J. W. Modestino, “Hyperspectral image compression using three-dimensional wavelet coding,” Proc. SPIE 5022, 1037–1047 (2003).
[Crossref]

X. Tang, S. Cho, and W. A. Pearlman, “3D set partitioning coding methods in hyperspectral image compression,” in International Conference on Image Processing ICIP (IEEE, 2003), p. II-239.

X. Tang and W. A. Pearlman, “Three-dimensional wavelet-based compression of hyperspectral images,” in Hyperspectral Data Compression (Springer, 2006), pp. 273–308.

Tao, D.

L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, “Compression of hyperspectral remote sensing images by tensor approach,” Neurocomputing 147, 358–363 (2015).
[Crossref]

Tillo, T.

B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
[Crossref]

Toderici, G.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” arXiv:1608.05148 (2016).

Töreyin, B. U.

I. Ülkü and B. U. Töreyin, “Sparse coding of hyperspectral imagery using online learning,” Signal Image Video Process. 9, 959–966 (2015).
[Crossref]

I. Ülkü and B. U. Töreyin, “Lossy compression of hyperspectral images using online learning based sparse coding,” in International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM) (IEEE, 2014), pp. 1–5.

Tucker, L. R.

L. R. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika 31, 279–311 (1966).
[Crossref]

Ulacha, G.

G. Ulacha and R. Stasiński, “New context-based adaptive linear prediction algorithm for lossless image coding,” in International Conference on Signals and Electronic Systems (ICSES) (IEEE, 2014), pp. 1–4.

Ulfarsson, M. O.

B. Rasti, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, “Hyperspectral image denoising using 3D wavelets,” in IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1349–1352.

Ülkü, I.

I. Ülkü and B. U. Töreyin, “Sparse coding of hyperspectral imagery using online learning,” Signal Image Video Process. 9, 959–966 (2015).
[Crossref]

I. Ülkü and B. U. Töreyin, “Lossy compression of hyperspectral images using online learning based sparse coding,” in International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM) (IEEE, 2014), pp. 1–5.

Veganzones, M. A.

M. A. Veganzones, J. E. Cohen, R. C. Farias, J. Chanussot, and P. Comon, “Nonnegative tensor CP decomposition of hyperspectral data,” IEEE Trans. Geosci. Remote Sens. 54, 2577–2588 (2016).
[Crossref]

Vincent, D.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” arXiv:1608.05148 (2016).

Vladimirova, T.

N. R. Mat Noor and T. Vladimirova, “Investigation into lossless hyperspectral image compression for satellite remote sensing,” Int. J. Remote Sens. 34, 5072–5104 (2013).
[Crossref]

Wang, C.

C. Wang, M. Gong, M. Zhang, and Y. Chan, “Unsupervised hyperspectral image band selection via column subset selection,” IEEE Geosci. Remote Sens. Lett. 12, 1411–1415 (2015).

Wang, F.

D. Zhao, S. Zhu, and F. Wang, “Lossy hyperspectral image compression based on intra-band prediction and inter-band fractal encoding,” Comput. Electr. Eng. 54, 494–505 (2016).
[Crossref]

S. Zhu, D. Zhao, and F. Wang, “Hybrid prediction and fractal hyperspectral image compression,” Math. Probl. Eng. 2015, 950357 (2015).
[Crossref]

Wang, H.

W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).

Wang, K.

J. Zhang, Y. Li, K. Wang, and H. Liu, “The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate,” Proc. SPIE 8514, 85140W (2012).
[Crossref]

Wang, L.

L. Wang, J. Wu, L. Jiao, and G. Shi, “Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT,” IEEE Geosci. Remote Sens. Lett. 6, 587–591 (2009).
[Crossref]

Wang, X.

F. Zhao, G. Liu, and X. Wang, “An efficient macroblock-based diverse and flexible prediction modes selection for hyperspectral images coding,” Signal Process. 25, 697–708 (2010).
[Crossref]

Wu, C.

J. Wu, Z. Wu, and C. Wu, “Lossy to lossless compressions of hyperspectral images using three-dimensional set partitioning algorithm,” Opt. Eng. 45, 027005 (2006).
[Crossref]

Wu, D.

H. Shen, W. D. Pan, and D. Wu, “Predictive lossless compression of regions of interest in hyperspectral images with no-data regions,” IEEE Trans. Geosci. Remote Sens. 55, 173–182 (2017).
[Crossref]

Wu, J.

L. Wang, J. Wu, L. Jiao, and G. Shi, “Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT,” IEEE Geosci. Remote Sens. Lett. 6, 587–591 (2009).
[Crossref]

J. Wu, Z. Wu, and C. Wu, “Lossy to lossless compressions of hyperspectral images using three-dimensional set partitioning algorithm,” Opt. Eng. 45, 027005 (2006).
[Crossref]

Wu, X.

X. Wu and N. Memon, “Context-based lossless interband compression-extending CALIC,” IEEE Trans. Image Process. 9, 994–1001 (2000).
[Crossref]

Wu, Z.

J. Wu, Z. Wu, and C. Wu, “Lossy to lossless compressions of hyperspectral images using three-dimensional set partitioning algorithm,” Opt. Eng. 45, 027005 (2006).
[Crossref]

Xiao, R.

M. Paul, R. Xiao, J. Gao, and T. Bossomaier, “Reflectance prediction modelling for residual-based hyperspectral image coding,” PloS one 11, e0161212 (2016).
[Crossref]

Xing, Z.

A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro, “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 49, 4263–4281 (2011).
[Crossref]

Yan, C.

F. Gao, X. Ji, C. Yan, and Q. Dai, “Compression of multispectral image using HEVC,” Proc. SPIE 9273, 92732X (2014).
[Crossref]

Yazdi, M.

A. Karami, M. Yazdi, and G. Mercier, “Compression of hyperspectral images using discerete wavelet transform and Tucker decomposition,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 444–450 (2012).
[Crossref]

Ye, M.

Y. Qian and M. Ye, “Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 6, 499–515 (2013).
[Crossref]

Y. Qian, M. Ye, and J. Zhou, “Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features,” IEEE Trans. Geosci. Remote Sens. 51, 2276–2291 (2013).
[Crossref]

Ye, Z.

Y. Nian, Y. Liu, and Z. Ye, “Pairwise KLT-based compression for multispectral images,” Sens. Imaging 17, 1–15 (2016).
[Crossref]

Yu, L.

W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
[Crossref]

Zala, M.

M. Zala and S. Parmar, “3D Wavelet transform with SPIHT algorithm for image compression,” Int. J. Appl. Innov. Eng. Manage. 2, 384–392 (2013).

Zayed, H. H.

H. H. Zayed, S. E. Kishk, and H. M. Ahmed, “3D wavelets with SPIHT coding for integral imaging compression,” Int. J. Comput. Sci. Netw. Secur. 12, 126–133 (2012).

Zhang, B.

W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).

Zhang, J.

J. Zhang, Y. Li, K. Wang, and H. Liu, “The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate,” Proc. SPIE 8514, 85140W (2012).
[Crossref]

Zhang, L.

L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, “Compression of hyperspectral remote sensing images by tensor approach,” Neurocomputing 147, 358–363 (2015).
[Crossref]

L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, “Compression of hyperspectral remote sensing images by tensor approach,” Neurocomputing 147, 358–363 (2015).
[Crossref]

Zhang, M.

C. Wang, M. Gong, M. Zhang, and Y. Chan, “Unsupervised hyperspectral image band selection via column subset selection,” IEEE Geosci. Remote Sens. Lett. 12, 1411–1415 (2015).

Zhao, C.

X. Li, J. Ren, C. Zhao, T. Qiao, and S. Marshall, “Novel multivariate vector quantization for effective compression of hyperspectral imagery,” Opt. Commun. 332, 192–200 (2014).
[Crossref]

Zhao, D.

D. Zhao, S. Zhu, and F. Wang, “Lossy hyperspectral image compression based on intra-band prediction and inter-band fractal encoding,” Comput. Electr. Eng. 54, 494–505 (2016).
[Crossref]

S. Zhu, D. Zhao, and F. Wang, “Hybrid prediction and fractal hyperspectral image compression,” Math. Probl. Eng. 2015, 950357 (2015).
[Crossref]

Zhao, F.

F. Zhao, G. Liu, and X. Wang, “An efficient macroblock-based diverse and flexible prediction modes selection for hyperspectral images coding,” Signal Process. 25, 697–708 (2010).
[Crossref]

Zhao, W.

W. Zhao and S. Du, “Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach,” IEEE Trans. Geosci. Remote Sens. 54, 4544–4554 (2016).
[Crossref]

Zhao, X. J.

X. J. Zhao and Y. F. Jing, “The application of vector quantization algorithm in hyperspectral image compression,” in Advanced Materials Research (Trans Tech Publication, 2013), pp. 1479–1483.

Zheng, J.

T. Qiao, J. Ren, M. Sun, J. Zheng, and S. Marshall, “Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications,” Int. J. Remote Sens. 35, 7316–7337 (2014).
[Crossref]

Zhou, J.

Y. Qian, M. Ye, and J. Zhou, “Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features,” IEEE Trans. Geosci. Remote Sens. 51, 2276–2291 (2013).
[Crossref]

Zhu, S.

D. Zhao, S. Zhu, and F. Wang, “Lossy hyperspectral image compression based on intra-band prediction and inter-band fractal encoding,” Comput. Electr. Eng. 54, 494–505 (2016).
[Crossref]

S. Zhu, D. Zhao, and F. Wang, “Hybrid prediction and fractal hyperspectral image compression,” Math. Probl. Eng. 2015, 950357 (2015).
[Crossref]

ACEEE Int. J. Inf. Technol. (1)

S. Raja and A. Suruliandi, “Image compression using WDR & ASWDR techniques with different wavelet codecs,” ACEEE Int. J. Inf. Technol. 1, 23–26 (2011).
[Crossref]

Appl. Mech. Mater. (1)

G. G. King, C. C. Seldev, and N. A. Singh, “A novel compression technique for compound images using parallel Lempel–Ziv–Welch algorithm,” Appl. Mech. Mater. 626, 44–51 (2014).
[Crossref]

Comput. Electr. Eng. (1)

D. Zhao, S. Zhu, and F. Wang, “Lossy hyperspectral image compression based on intra-band prediction and inter-band fractal encoding,” Comput. Electr. Eng. 54, 494–505 (2016).
[Crossref]

EURASIP J. Adv. Signal Process. (1)

M. F. M. Salleh and J. Soraghan, “A new multistage lattice vector quantization with adaptive subband thresholding for image compression,” EURASIP J. Adv. Signal Process. 2007, 092928 (2007).
[Crossref]

IEEE Geosci. Remote Sens. Lett. (10)

J. Gonzalez-Conejero, J. Bartrina-Rapesta, and J. Serra-Sagrista, “JPEG2000 encoding of remote sensing multispectral images with no-data regions,” IEEE Geosci. Remote Sens. Lett. 7, 251–255 (2010).
[Crossref]

Q. Du and J. E. Fowler, “Hyperspectral image compression using JPEG2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4, 201–205 (2007).
[Crossref]

L. Wang, J. Wu, L. Jiao, and G. Shi, “Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT,” IEEE Geosci. Remote Sens. Lett. 6, 587–591 (2009).
[Crossref]

X. Pan, R. Liu, and X. Lv, “Low-complexity compression method for hyperspectral images based on distributed source coding,” IEEE Geosci. Remote Sens. Lett. 9, 224–227 (2012).
[Crossref]

J. Mielikainen and B. Huang, “Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length,” IEEE Geosci. Remote Sens. Lett. 9, 1118–1121 (2012).
[Crossref]

E. Magli, G. Olmo, and E. Quacchio, “Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC,” IEEE Geosci. Remote Sens. Lett. 1, 21–25 (2004).
[Crossref]

C.-C. Lin and Y.-T. Hwang, “An efficient lossless compression scheme for hyperspectral images using two-stage prediction,” IEEE Geosci. Remote Sens. Lett. 7, 558–562 (2010).
[Crossref]

N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep learning classification of land cover and crop types using remote sensing data,” IEEE Geosci. Remote Sens. Lett. 14, 778–782 (2017).
[Crossref]

D. Marmanis, M. Datcu, T. Esch, and U. Stilla, “Deep learning earth observation classification using ImageNet pretrained networks,” IEEE Geosci. Remote Sens. Lett. 13, 105–109 (2016).
[Crossref]

C. Wang, M. Gong, M. Zhang, and Y. Chan, “Unsupervised hyperspectral image band selection via column subset selection,” IEEE Geosci. Remote Sens. Lett. 12, 1411–1415 (2015).

IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. (3)

L. Santos, S. Lopez, G. M. Callico, J. F. Lopez, and R. Sarmiento, “Performance evaluation of the H. 264/AVC video coding standard for lossy hyperspectral image compression,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 451–461 (2012).
[Crossref]

Y. Qian and M. Ye, “Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 6, 499–515 (2013).
[Crossref]

A. Karami, M. Yazdi, and G. Mercier, “Compression of hyperspectral images using discerete wavelet transform and Tucker decomposition,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 444–450 (2012).
[Crossref]

IEEE J. Sel. Top. Signal Process. (1)

A. S. Charles, B. A. Olshausen, and C. J. Rozell, “Learning sparse codes for hyperspectral imagery,” IEEE J. Sel. Top. Signal Process. 5, 963–978 (2011).
[Crossref]

IEEE Signal Process. Mag. (1)

A. Skodras, C. Christopoulos, and T. Ebrahimi, “The JPEG 2000 still image compression standard,” IEEE Signal Process. Mag. 18(5), 36–58 (2001).
[Crossref]

IEEE Trans. Geosci. Remote Sens. (11)

M. A. Veganzones, J. E. Cohen, R. C. Farias, J. Chanussot, and P. Comon, “Nonnegative tensor CP decomposition of hyperspectral data,” IEEE Trans. Geosci. Remote Sens. 54, 2577–2588 (2016).
[Crossref]

I. Blanes and J. Serra-Sagristà, “Cost and scalability improvements to the Karhunen–Loêve transform for remote-sensing image coding,” IEEE Trans. Geosci. Remote Sens. 48, 2854–2863 (2010).
[Crossref]

B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
[Crossref]

A. Karami, R. Heylen, and P. Scheunders, “Hyperspectral image compression optimized for spectral unmixing,” IEEE Trans. Geosci. Remote Sens. 54, 5884–5894 (2016).
[Crossref]

H. Shen, W. D. Pan, and D. Wu, “Predictive lossless compression of regions of interest in hyperspectral images with no-data regions,” IEEE Trans. Geosci. Remote Sens. 55, 173–182 (2017).
[Crossref]

Y. Qian, M. Ye, and J. Zhou, “Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features,” IEEE Trans. Geosci. Remote Sens. 51, 2276–2291 (2013).
[Crossref]

N. Amrani, J. Serra-Sagristà, V. Laparra, M. W. Marcellin, and J. Malo, “Regression wavelet analysis for lossless coding of remote-sensing data,” IEEE Trans. Geosci. Remote Sens. 54, 5616–5627 (2016).
[Crossref]

A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin, and G. Sapiro, “Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 49, 4263–4281 (2011).
[Crossref]

A. Romero, C. Gatta, and G. Camps-Valls, “Unsupervised deep feature extraction for remote sensing image classification,” IEEE Trans. Geosci. Remote Sens. 54, 1349–1362 (2016).
[Crossref]

W. Zhao and S. Du, “Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach,” IEEE Trans. Geosci. Remote Sens. 54, 4544–4554 (2016).
[Crossref]

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54, 6232–6251 (2016).
[Crossref]

IEEE Trans. Image Process. (1)

X. Wu and N. Memon, “Context-based lossless interband compression-extending CALIC,” IEEE Trans. Image Process. 9, 994–1001 (2000).
[Crossref]

Image Vision Comput. (1)

G. Dudek, P. Borys, and Z. J. Grzywna, “Lossy dictionary-based image compression method,” Image Vision Comput. 25, 883–889 (2007).
[Crossref]

Int. J. Appl. Innov. Eng. Manage. (1)

M. Zala and S. Parmar, “3D Wavelet transform with SPIHT algorithm for image compression,” Int. J. Appl. Innov. Eng. Manage. 2, 384–392 (2013).

Int. J. Comput. Sci. Commun. (1)

V. Shingate, T. Sontakke, and S. Talbar, “Still image compression using embedded zerotree wavelet encoding,” Int. J. Comput. Sci. Commun. 1, 21–24 (2010).

Int. J. Comput. Sci. Netw. Secur. (1)

H. H. Zayed, S. E. Kishk, and H. M. Ahmed, “3D wavelets with SPIHT coding for integral imaging compression,” Int. J. Comput. Sci. Netw. Secur. 12, 126–133 (2012).

Int. J. Remote Sens. (3)

N. R. Mat Noor and T. Vladimirova, “Investigation into lossless hyperspectral image compression for satellite remote sensing,” Int. J. Remote Sens. 34, 5072–5104 (2013).
[Crossref]

T. Qiao, J. Ren, M. Sun, J. Zheng, and S. Marshall, “Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications,” Int. J. Remote Sens. 35, 7316–7337 (2014).
[Crossref]

W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping,” Int. J. Remote Sens. 37, 5632–5646 (2016).
[Crossref]

J. Digit. Imaging (1)

W. F. Good, G. S. Maitz, and D. Gur, “Joint Photographic Experts Group (JPEG) compatible data compression of mammograms,” J. Digit. Imaging 7, 123–132 (1994).

J. Inf. Sci. Eng. (1)

C.-C. Lin and Y.-T. Hwang, “Lossless compression of hyperspectral images using adaptive prediction and backward search schemes,” J. Inf. Sci. Eng. 27, 419–435 (2011).

J. Opt. Soc. Am. A (1)

Math. Probl. Eng. (1)

S. Zhu, D. Zhao, and F. Wang, “Hybrid prediction and fractal hyperspectral image compression,” Math. Probl. Eng. 2015, 950357 (2015).
[Crossref]

Multimed. Tools Appl. (1)

W. Jifara, F. Jiang, B. Zhang, H. Wang, J. Li, A. Grigorev, and S. Liu, “Hyperspectral image compression based on online learning spectral features dictionary,” Multimed. Tools Appl. 76, 1–12 (2017).

Neurocomputing (1)

L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, “Compression of hyperspectral remote sensing images by tensor approach,” Neurocomputing 147, 358–363 (2015).
[Crossref]

Opt. Commun. (1)

X. Li, J. Ren, C. Zhao, T. Qiao, and S. Marshall, “Novel multivariate vector quantization for effective compression of hyperspectral imagery,” Opt. Commun. 332, 192–200 (2014).
[Crossref]

Opt. Eng. (1)

J. Wu, Z. Wu, and C. Wu, “Lossy to lossless compressions of hyperspectral images using three-dimensional set partitioning algorithm,” Opt. Eng. 45, 027005 (2006).
[Crossref]

PloS one (1)

M. Paul, R. Xiao, J. Gao, and T. Bossomaier, “Reflectance prediction modelling for residual-based hyperspectral image coding,” PloS one 11, e0161212 (2016).
[Crossref]

Proc. SPIE (3)

F. Gao, X. Ji, C. Yan, and Q. Dai, “Compression of multispectral image using HEVC,” Proc. SPIE 9273, 92732X (2014).
[Crossref]

X. Tang, W. A. Pearlman, and J. W. Modestino, “Hyperspectral image compression using three-dimensional wavelet coding,” Proc. SPIE 5022, 1037–1047 (2003).
[Crossref]

J. Zhang, Y. Li, K. Wang, and H. Liu, “The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate,” Proc. SPIE 8514, 85140W (2012).
[Crossref]

Psychometrika (2)

J. D. Carroll and J.-J. Chang, “Analysis of individual differences in multidimensional scaling via an N-way generalization of ‘Eckart–Young’ decomposition,” Psychometrika 35, 283–319 (1970).
[Crossref]

L. R. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika 31, 279–311 (1966).
[Crossref]

Sens. Imaging (1)

Y. Nian, Y. Liu, and Z. Ye, “Pairwise KLT-based compression for multispectral images,” Sens. Imaging 17, 1–15 (2016).
[Crossref]

Signal Image Video Process. (1)

I. Ülkü and B. U. Töreyin, “Sparse coding of hyperspectral imagery using online learning,” Signal Image Video Process. 9, 959–966 (2015).
[Crossref]

Signal Process. (1)

F. Zhao, G. Liu, and X. Wang, “An efficient macroblock-based diverse and flexible prediction modes selection for hyperspectral images coding,” Signal Process. 25, 697–708 (2010).
[Crossref]

Stud. Appl. Math. (1)

F. L. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,” Stud. Appl. Math. 6, 164–189 (1927).
[Crossref]

Other (26)

R. A. Harshman, “Foundations of the PARAFAC procedure: models and conditions for an ‘explanatory’ multi-modal factor analysis,” in UCLA Working Papers in Phonetics (1970), Vol. 16, pp. 1–84.

S. Shahriyar, M. Paul, M. Murshed, and M. Ali, “Lossless hyperspectral image compression using binary tree based decomposition,” in International Conference on Digital Image Computing: Techniques and Applications (DICTA) (IEEE, 2016), pp. 1–8.

Q. Du, N. Ly, and J. E. Fowler, “An operational approach for hyperspectral image compression,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1357–1360.

K. Sayood, Introduction to Data Compression (Newnes, 2012).

“Vector quantization,” 2016, http://www.data-compression.com/vq.html .

X. J. Zhao and Y. F. Jing, “The application of vector quantization algorithm in hyperspectral image compression,” in Advanced Materials Research (Trans Tech Publication, 2013), pp. 1479–1483.

R. George and M. Manimekalai, “A novel approach for image compression using zero tree coding,” in International Conference on Electronics and Communication Systems (ICECS) (IEEE, 2014), pp. 1–5.

R. B. Smith, “Introduction to hyperspectral imaging,” Microimages, 2006, http://www.microimages.com/documentation/Tutorials/hyprspec.pdf .

G. Motta, F. Rizzo, and J. A. Storer, Hyperspectral Data Compression (Springer, 2006).

A. C. Bovik, Handbook of Image and Video Processing (Academic, 2010).

S. Shukla and M. Prasad, Lossy Image Compression: Domain Decomposition-Based Algorithms (Springer, 2011).

C.-I. Chang, Hyperspectral Data Processing: Algorithm Design and Analysis (Wiley, 2013).

R. Dusselaar, M. Paul, and T. Bossomaier, “Hyperspectral image coding using spectral prediction modelling in HEVC coding framework,” in International Conference on Image and Vision Computing New Zealand (IVCNZ) (IEEE, 2015), pp. 1–6.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks,” arXiv:1608.05148 (2016).

Consultative Committee for Space Data Systems (CCSDS), “Lossless data compression,” , Blue Book (1997).

Consultative Committee for Space Data Systems (CCSDS), “Image data compression,” , Blue Book (2005).

Consultative Committee for Space Data Systems (CCSDS), “Lossless multispectral and hyperspectral image compression,” , Blue Book (2012).

I. Ülkü and B. U. Töreyin, “Lossy compression of hyperspectral images using online learning based sparse coding,” in International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM) (IEEE, 2014), pp. 1–5.

X. Tang, S. Cho, and W. A. Pearlman, “3D set partitioning coding methods in hyperspectral image compression,” in International Conference on Image Processing ICIP (IEEE, 2003), p. II-239.

G. Ulacha and R. Stasiński, “New context-based adaptive linear prediction algorithm for lossless image coding,” in International Conference on Signals and Electronic Systems (ICSES) (IEEE, 2014), pp. 1–4.

B. Rasti, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, “Hyperspectral image denoising using 3D wavelets,” in IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2012), pp. 1349–1352.

X. Tang and W. A. Pearlman, “Three-dimensional wavelet-based compression of hyperspectral images,” in Hyperspectral Data Compression (Springer, 2006), pp. 273–308.

“Imec snapshot hyperspectral imaging camera demonstration,” 2013, https://vimeo.com/64705346 .

“A breakthrough in precision farming,” 2017, https://www.questuav.com/media/case-study/multispectral-imaging-questuav-micasense-pix4dmapper-questuav-news/ .

P. K. Podder, M. Paul, and M. Murshed, “Efficient coding strategy for HEVC performance improvement by exploiting motion features,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2015), pp. 1414–1418.

P. J. Pabich, Hyperspectral Imagery: Warfighting Through a Different Set of Eyes (Defense Technical Information Center, 2002).

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Figures (4)

Fig. 1.
Fig. 1. Block diagram of the general categorization of the compression methods.
Fig. 2.
Fig. 2. Spectral/spatial HS image compression procedure.
Fig. 3.
Fig. 3. Example of a two-dimensional VQ. Source: [31].
Fig. 4.
Fig. 4. Rate-distortion performance of eight HS images using the JPEG2000, JPEG, PCA-DCT, and HEVC encoder techniques.

Tables (3)

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Table 1. Summary of Major Contributions and Challenges of Existing HS Image Compression Approaches

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Table 2. Publicly Available HS Datasets

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Table 3. Rate-Distortion Performance of Four HS Images Using Benchmark Methods

Equations (1)

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PSNR=10×log10(Peaki2MSE),

Metrics