Abstract

Extracting endmembers from remotely-sensed images of vegetated areas can present difficulties. In this research, we applied a recently-developed endmember-extraction algorithm based on Support Vector Machines to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically estimate endmembers; synthetic data and a geologic scene were previously analyzed. Here we compared the efficacies of SVM-BEE, N-FINDR, and SMACC algorithms in extracting endmembers from a real, predominantly-agricultural scene. SVM-BEE estimated vegetation and other endmembers for all classes in the image, which N-FINDR and SMACC failed to do. SVM-BEE was consistent in the endmembers that it estimated across replicate trials. Spectral angle mapper (SAM) classifications based on SVM-BEE-estimated endmembers were significantly more accurate compared with those based on N-FINDR- and (in general) SMACC-endmembers. Linear spectral unmixing accrued overall accuracies similar to those of SAM.

© 2009 OSA

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2009 (3)

A. M. Filippi and R. Archibald, “Support Vector Machine-Based Endmember Extraction,” IEEE Trans. Geosci. Rem. Sens. 47(3), 771–791 (2009).
[CrossRef]

B. Somers, S. Delalieux, J. Stuckens, W. W. Verstraeten, and P. Coppin, “A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems,” Int. J. Remote Sens. 30(1), 139–147 (2009).
[CrossRef]

M. Zortea and A. Plaza, “Spatial preprocessing for endmember extraction,” IEEE Trans. Geosci. Rem. Sens. 47(8), 2679–2693 (2009).
[CrossRef]

2008 (2)

A. Zare and P. Gader, “Hyperspectral band selection and endmember detection using sparsity promoting priors,” IEEE Geosci. Remote Sens. Lett. 5(2), 256–260 (2008).
[CrossRef]

J. Zhang, B. Rivard, and D. M. Rogge, “The Successive Projection Algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data,” Sensors 8(2), 1321–1342 (2008).
[CrossRef]

2007 (1)

H. Z. M. Shafri, A. Suhaili, and S. Mansor, “The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis,” J. Computer Sci. 3(6), 419–423 (2007).
[CrossRef]

2006 (7)

A. Plaza, D. Valencia, J. Plaza, and P. Martinez, “Commodity cluster-based parallel processing of hyperspectral imagery,” J. Parallel Distrib. Comput. 66(3), 345–358 (2006).
[CrossRef]

C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, “A new growing method for simplex-based endmember extraction algorithm,” IEEE Trans. Geosci. Rem. Sens. 44(10), 2804–2819 (2006).
[CrossRef]

C. Kwan, B. Ayhan, G. Chen, J. Wang, B. Ji, and C.-I. Chang, “A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents,” IEEE Trans. Geosci. Rem. Sens. 44(2), 409–419 (2006).
[CrossRef]

E. Ramsey and A. Rangoonwala, “Canopy reflectance related to marsh dieback onset and progression in coastal Louisiana,” Photogramm. Eng. Remote Sensing 72(6), 641–652 (2006).

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

A. Plaza and C.-I. Chang, “Impact of initialization on design of endmember extraction algorithms,” IEEE Trans. Geosci. Rem. Sens. 44(11), 3397–3407 (2006).
[CrossRef]

A. M. Filippi and J. R. Jensen, “Fuzzy learning vector quantization for hyperspectral coastal vegetation classification,” Remote Sens. Environ. 100(4), 512–530 (2006).
[CrossRef]

2005 (4)

E. Ramsey, A. Rangoonwala, G. Nelson, R. Ehrlich, and K. Martella, “Generation and validation of characteristic spectra from EO1 Hyperion image data for detecting the occurrence of the invasive species, Chinese tallow,” Int. J. Remote Sens. 26(8), 1611–1636 (2005).
[CrossRef]

D. R. Peddle and M. Smith, “Spectral mixture analysis of agricultural crops: endmember validation and biophysical estimation in potato plots,” Int. J. Remote Sens. 26(22), 4959–4979 (2005).
[CrossRef]

M. S. A. C. Marcos, M. N. Soriano, and C. A. Saloma, “Classification of coral reef images from underwater video using neural networks,” Opt. Express 13(22), 8766–8771 (2005), http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-13-22-8766 .
[CrossRef] [PubMed]

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral classification,” IEEE Trans. Geosci. Rem. Sens. 43(6), 1351–1362 (2005).
[CrossRef]

2004 (4)

C.-I. Chang and Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Rem. Sens. 42(3), 608–619 (2004).
[CrossRef]

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43(8), 1777–1786 (2004).
[CrossRef]

J. Gruninger, A. J. Ratkowski, and M. L. Hoke, “The sequential maximum angle convex cone (SMACC) endmember model,” Proc. SPIE 5425, 1–14 (2004).
[CrossRef]

A. Plaza, P. Martínez, R. Pérez, and J. Plaza, “A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data,” IEEE Trans. Geosci. Rem. Sens. 42(3), 650–663 (2004).
[CrossRef]

2003 (1)

N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Laboratory J. 14(1), 55–78 (2003).

2000 (3)

M. Brown, H. G. Lewis, and S. R. Gunn, “Linear mixture models and support vector machines for remote sensing,” IEEE Trans. Geosci. Rem. Sens. 38(5), 2346–2360 (2000).
[CrossRef]

G. P. Asner and D. B. Lobell, “A biogeophysical approach for automated SWIR unmixing of soils and vegetation,” Remote Sens. Environ. 74(1), 99–112 (2000).
[CrossRef]

C. A. Bateson, G. P. Asner, and C. A. Wessman, “Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis,” IEEE Trans. Geosci. Rem. Sens. 38(2), 1083–1094 (2000).
[CrossRef]

1999 (2)

D. M. J. Tax and R. P. W. Duin, “Support vector domain description,” Pattern Recognit. Lett. 20(11-13), 1191–1199 (1999).
[CrossRef]

M. E. Winter, “N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data,” Proc. SPIE 3753, 266–275 (1999).
[CrossRef]

1998 (1)

G. P. Asner, “Biophysical and biochemical sources of variability in canopy reflectance,” Remote Sens. Environ. 64(3), 234–253 (1998).
[CrossRef]

1994 (2)

C. C. Borel and S. A. W. Gerstl, “Nonlinear spectral mixing models for vegetative and soil surfaces,” Remote Sens. Environ. 47(3), 403–416 (1994).
[CrossRef]

J. C. Price, “How unique are spectral signatures?” Remote Sens. Environ. 49(3), 181–186 (1994).
[CrossRef]

1993 (2)

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ. 44(2–3), 127–143 (1993).
[CrossRef]

F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data,” Remote Sens. Environ. 44(2–3), 145–163 (1993).
[CrossRef]

1991 (1)

R. G. Congalton, “A review of assessing the accuracy of classifications of remotely sensed data,” Remote Sens. Environ. 37(1), 35–46 (1991).
[CrossRef]

1988 (1)

A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Trans. Geosci. Rem. Sens. 26(1), 65–74 (1988).
[CrossRef]

Anderson, G. L.

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

Archibald, R.

A. M. Filippi and R. Archibald, “Support Vector Machine-Based Endmember Extraction,” IEEE Trans. Geosci. Rem. Sens. 47(3), 771–791 (2009).
[CrossRef]

Asner, G. P.

C. A. Bateson, G. P. Asner, and C. A. Wessman, “Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis,” IEEE Trans. Geosci. Rem. Sens. 38(2), 1083–1094 (2000).
[CrossRef]

G. P. Asner and D. B. Lobell, “A biogeophysical approach for automated SWIR unmixing of soils and vegetation,” Remote Sens. Environ. 74(1), 99–112 (2000).
[CrossRef]

G. P. Asner, “Biophysical and biochemical sources of variability in canopy reflectance,” Remote Sens. Environ. 64(3), 234–253 (1998).
[CrossRef]

Ayhan, B.

C. Kwan, B. Ayhan, G. Chen, J. Wang, B. Ji, and C.-I. Chang, “A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents,” IEEE Trans. Geosci. Rem. Sens. 44(2), 409–419 (2006).
[CrossRef]

Barloon, P.

F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data,” Remote Sens. Environ. 44(2–3), 145–163 (1993).
[CrossRef]

Bateson, C. A.

C. A. Bateson, G. P. Asner, and C. A. Wessman, “Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis,” IEEE Trans. Geosci. Rem. Sens. 38(2), 1083–1094 (2000).
[CrossRef]

Berman, M.

A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Trans. Geosci. Rem. Sens. 26(1), 65–74 (1988).
[CrossRef]

Boardman, J.

F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data,” Remote Sens. Environ. 44(2–3), 145–163 (1993).
[CrossRef]

Borel, C. C.

C. C. Borel and S. A. W. Gerstl, “Nonlinear spectral mixing models for vegetative and soil surfaces,” Remote Sens. Environ. 47(3), 403–416 (1994).
[CrossRef]

Brown, M.

M. Brown, H. G. Lewis, and S. R. Gunn, “Linear mixture models and support vector machines for remote sensing,” IEEE Trans. Geosci. Rem. Sens. 38(5), 2346–2360 (2000).
[CrossRef]

Bruzzone, L.

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral classification,” IEEE Trans. Geosci. Rem. Sens. 43(6), 1351–1362 (2005).
[CrossRef]

Camps-Valls, G.

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral classification,” IEEE Trans. Geosci. Rem. Sens. 43(6), 1351–1362 (2005).
[CrossRef]

Carruthers, R.

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

Chang, C.-C.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43(8), 1777–1786 (2004).
[CrossRef]

Chang, C.-I.

C. Kwan, B. Ayhan, G. Chen, J. Wang, B. Ji, and C.-I. Chang, “A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents,” IEEE Trans. Geosci. Rem. Sens. 44(2), 409–419 (2006).
[CrossRef]

C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, “A new growing method for simplex-based endmember extraction algorithm,” IEEE Trans. Geosci. Rem. Sens. 44(10), 2804–2819 (2006).
[CrossRef]

A. Plaza and C.-I. Chang, “Impact of initialization on design of endmember extraction algorithms,” IEEE Trans. Geosci. Rem. Sens. 44(11), 3397–3407 (2006).
[CrossRef]

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43(8), 1777–1786 (2004).
[CrossRef]

C.-I. Chang and Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Rem. Sens. 42(3), 608–619 (2004).
[CrossRef]

Chen, G.

C. Kwan, B. Ayhan, G. Chen, J. Wang, B. Ji, and C.-I. Chang, “A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents,” IEEE Trans. Geosci. Rem. Sens. 44(2), 409–419 (2006).
[CrossRef]

Chrien, T. G.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ. 44(2–3), 127–143 (1993).
[CrossRef]

Congalton, R. G.

R. G. Congalton, “A review of assessing the accuracy of classifications of remotely sensed data,” Remote Sens. Environ. 37(1), 35–46 (1991).
[CrossRef]

Coppin, P.

B. Somers, S. Delalieux, J. Stuckens, W. W. Verstraeten, and P. Coppin, “A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems,” Int. J. Remote Sens. 30(1), 139–147 (2009).
[CrossRef]

Craig, M. D.

A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Trans. Geosci. Rem. Sens. 26(1), 65–74 (1988).
[CrossRef]

D’Amico, F. M.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43(8), 1777–1786 (2004).
[CrossRef]

Delalieux, S.

B. Somers, S. Delalieux, J. Stuckens, W. W. Verstraeten, and P. Coppin, “A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems,” Int. J. Remote Sens. 30(1), 139–147 (2009).
[CrossRef]

Du, Q.

C.-I. Chang and Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Rem. Sens. 42(3), 608–619 (2004).
[CrossRef]

Du, Y.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43(8), 1777–1786 (2004).
[CrossRef]

Duin, R. P. W.

D. M. J. Tax and R. P. W. Duin, “Support vector domain description,” Pattern Recognit. Lett. 20(11-13), 1191–1199 (1999).
[CrossRef]

Ehrlich, R.

E. Ramsey, A. Rangoonwala, G. Nelson, R. Ehrlich, and K. Martella, “Generation and validation of characteristic spectra from EO1 Hyperion image data for detecting the occurrence of the invasive species, Chinese tallow,” Int. J. Remote Sens. 26(8), 1611–1636 (2005).
[CrossRef]

Enmark, H. T.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ. 44(2–3), 127–143 (1993).
[CrossRef]

Filippi, A. M.

A. M. Filippi and R. Archibald, “Support Vector Machine-Based Endmember Extraction,” IEEE Trans. Geosci. Rem. Sens. 47(3), 771–791 (2009).
[CrossRef]

A. M. Filippi and J. R. Jensen, “Fuzzy learning vector quantization for hyperspectral coastal vegetation classification,” Remote Sens. Environ. 100(4), 512–530 (2006).
[CrossRef]

Gader, P.

A. Zare and P. Gader, “Hyperspectral band selection and endmember detection using sparsity promoting priors,” IEEE Geosci. Remote Sens. Lett. 5(2), 256–260 (2008).
[CrossRef]

Gerstl, S. A. W.

C. C. Borel and S. A. W. Gerstl, “Nonlinear spectral mixing models for vegetative and soil surfaces,” Remote Sens. Environ. 47(3), 403–416 (1994).
[CrossRef]

Goetz, A.

F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data,” Remote Sens. Environ. 44(2–3), 145–163 (1993).
[CrossRef]

Gong, P.

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

Green, A. A.

A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Trans. Geosci. Rem. Sens. 26(1), 65–74 (1988).
[CrossRef]

Green, R. O.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ. 44(2–3), 127–143 (1993).
[CrossRef]

Gruninger, J.

J. Gruninger, A. J. Ratkowski, and M. L. Hoke, “The sequential maximum angle convex cone (SMACC) endmember model,” Proc. SPIE 5425, 1–14 (2004).
[CrossRef]

Gunn, S. R.

M. Brown, H. G. Lewis, and S. R. Gunn, “Linear mixture models and support vector machines for remote sensing,” IEEE Trans. Geosci. Rem. Sens. 38(5), 2346–2360 (2000).
[CrossRef]

Hansen, E. G.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ. 44(2–3), 127–143 (1993).
[CrossRef]

Heaton, J. S.

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

Heidebrecht, K.

F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data,” Remote Sens. Environ. 44(2–3), 145–163 (1993).
[CrossRef]

Hoke, M. L.

J. Gruninger, A. J. Ratkowski, and M. L. Hoke, “The sequential maximum angle convex cone (SMACC) endmember model,” Proc. SPIE 5425, 1–14 (2004).
[CrossRef]

Jensen, J. O.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43(8), 1777–1786 (2004).
[CrossRef]

Jensen, J. R.

A. M. Filippi and J. R. Jensen, “Fuzzy learning vector quantization for hyperspectral coastal vegetation classification,” Remote Sens. Environ. 100(4), 512–530 (2006).
[CrossRef]

Ji, B.

C. Kwan, B. Ayhan, G. Chen, J. Wang, B. Ji, and C.-I. Chang, “A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents,” IEEE Trans. Geosci. Rem. Sens. 44(2), 409–419 (2006).
[CrossRef]

Keshava, N.

N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Laboratory J. 14(1), 55–78 (2003).

Kruse, F. A.

F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data,” Remote Sens. Environ. 44(2–3), 145–163 (1993).
[CrossRef]

Kwan, C.

C. Kwan, B. Ayhan, G. Chen, J. Wang, B. Ji, and C.-I. Chang, “A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents,” IEEE Trans. Geosci. Rem. Sens. 44(2), 409–419 (2006).
[CrossRef]

Lefkoff, A.

F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data,” Remote Sens. Environ. 44(2–3), 145–163 (1993).
[CrossRef]

Lewis, H. G.

M. Brown, H. G. Lewis, and S. R. Gunn, “Linear mixture models and support vector machines for remote sensing,” IEEE Trans. Geosci. Rem. Sens. 38(5), 2346–2360 (2000).
[CrossRef]

Liu, W.

C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, “A new growing method for simplex-based endmember extraction algorithm,” IEEE Trans. Geosci. Rem. Sens. 44(10), 2804–2819 (2006).
[CrossRef]

Lobell, D. B.

G. P. Asner and D. B. Lobell, “A biogeophysical approach for automated SWIR unmixing of soils and vegetation,” Remote Sens. Environ. 74(1), 99–112 (2000).
[CrossRef]

Mansor, S.

H. Z. M. Shafri, A. Suhaili, and S. Mansor, “The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis,” J. Computer Sci. 3(6), 419–423 (2007).
[CrossRef]

Marcos, M. S. A. C.

Martella, K.

E. Ramsey, A. Rangoonwala, G. Nelson, R. Ehrlich, and K. Martella, “Generation and validation of characteristic spectra from EO1 Hyperion image data for detecting the occurrence of the invasive species, Chinese tallow,” Int. J. Remote Sens. 26(8), 1611–1636 (2005).
[CrossRef]

Martinez, P.

A. Plaza, D. Valencia, J. Plaza, and P. Martinez, “Commodity cluster-based parallel processing of hyperspectral imagery,” J. Parallel Distrib. Comput. 66(3), 345–358 (2006).
[CrossRef]

Martínez, P.

A. Plaza, P. Martínez, R. Pérez, and J. Plaza, “A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data,” IEEE Trans. Geosci. Rem. Sens. 42(3), 650–663 (2004).
[CrossRef]

Miao, X.

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

Nelson, G.

E. Ramsey, A. Rangoonwala, G. Nelson, R. Ehrlich, and K. Martella, “Generation and validation of characteristic spectra from EO1 Hyperion image data for detecting the occurrence of the invasive species, Chinese tallow,” Int. J. Remote Sens. 26(8), 1611–1636 (2005).
[CrossRef]

Ouyang, Y.-C.

C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, “A new growing method for simplex-based endmember extraction algorithm,” IEEE Trans. Geosci. Rem. Sens. 44(10), 2804–2819 (2006).
[CrossRef]

Peddle, D. R.

D. R. Peddle and M. Smith, “Spectral mixture analysis of agricultural crops: endmember validation and biophysical estimation in potato plots,” Int. J. Remote Sens. 26(22), 4959–4979 (2005).
[CrossRef]

Pérez, R.

A. Plaza, P. Martínez, R. Pérez, and J. Plaza, “A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data,” IEEE Trans. Geosci. Rem. Sens. 42(3), 650–663 (2004).
[CrossRef]

Plaza, A.

M. Zortea and A. Plaza, “Spatial preprocessing for endmember extraction,” IEEE Trans. Geosci. Rem. Sens. 47(8), 2679–2693 (2009).
[CrossRef]

A. Plaza and C.-I. Chang, “Impact of initialization on design of endmember extraction algorithms,” IEEE Trans. Geosci. Rem. Sens. 44(11), 3397–3407 (2006).
[CrossRef]

A. Plaza, D. Valencia, J. Plaza, and P. Martinez, “Commodity cluster-based parallel processing of hyperspectral imagery,” J. Parallel Distrib. Comput. 66(3), 345–358 (2006).
[CrossRef]

A. Plaza, P. Martínez, R. Pérez, and J. Plaza, “A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data,” IEEE Trans. Geosci. Rem. Sens. 42(3), 650–663 (2004).
[CrossRef]

Plaza, J.

A. Plaza, D. Valencia, J. Plaza, and P. Martinez, “Commodity cluster-based parallel processing of hyperspectral imagery,” J. Parallel Distrib. Comput. 66(3), 345–358 (2006).
[CrossRef]

A. Plaza, P. Martínez, R. Pérez, and J. Plaza, “A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data,” IEEE Trans. Geosci. Rem. Sens. 42(3), 650–663 (2004).
[CrossRef]

Porter, W. M.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ. 44(2–3), 127–143 (1993).
[CrossRef]

Price, J. C.

J. C. Price, “How unique are spectral signatures?” Remote Sens. Environ. 49(3), 181–186 (1994).
[CrossRef]

Pu, R.

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

Ramsey, E.

E. Ramsey and A. Rangoonwala, “Canopy reflectance related to marsh dieback onset and progression in coastal Louisiana,” Photogramm. Eng. Remote Sensing 72(6), 641–652 (2006).

E. Ramsey, A. Rangoonwala, G. Nelson, R. Ehrlich, and K. Martella, “Generation and validation of characteristic spectra from EO1 Hyperion image data for detecting the occurrence of the invasive species, Chinese tallow,” Int. J. Remote Sens. 26(8), 1611–1636 (2005).
[CrossRef]

Rangoonwala, A.

E. Ramsey and A. Rangoonwala, “Canopy reflectance related to marsh dieback onset and progression in coastal Louisiana,” Photogramm. Eng. Remote Sensing 72(6), 641–652 (2006).

E. Ramsey, A. Rangoonwala, G. Nelson, R. Ehrlich, and K. Martella, “Generation and validation of characteristic spectra from EO1 Hyperion image data for detecting the occurrence of the invasive species, Chinese tallow,” Int. J. Remote Sens. 26(8), 1611–1636 (2005).
[CrossRef]

Ratkowski, A. J.

J. Gruninger, A. J. Ratkowski, and M. L. Hoke, “The sequential maximum angle convex cone (SMACC) endmember model,” Proc. SPIE 5425, 1–14 (2004).
[CrossRef]

Ren, H.

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43(8), 1777–1786 (2004).
[CrossRef]

Rivard, B.

J. Zhang, B. Rivard, and D. M. Rogge, “The Successive Projection Algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data,” Sensors 8(2), 1321–1342 (2008).
[CrossRef]

Rogge, D. M.

J. Zhang, B. Rivard, and D. M. Rogge, “The Successive Projection Algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data,” Sensors 8(2), 1321–1342 (2008).
[CrossRef]

Saloma, C. A.

Shafri, H. Z. M.

H. Z. M. Shafri, A. Suhaili, and S. Mansor, “The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis,” J. Computer Sci. 3(6), 419–423 (2007).
[CrossRef]

Shapiro, A.

F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data,” Remote Sens. Environ. 44(2–3), 145–163 (1993).
[CrossRef]

Smith, M.

D. R. Peddle and M. Smith, “Spectral mixture analysis of agricultural crops: endmember validation and biophysical estimation in potato plots,” Int. J. Remote Sens. 26(22), 4959–4979 (2005).
[CrossRef]

Somers, B.

B. Somers, S. Delalieux, J. Stuckens, W. W. Verstraeten, and P. Coppin, “A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems,” Int. J. Remote Sens. 30(1), 139–147 (2009).
[CrossRef]

Soriano, M. N.

Stuckens, J.

B. Somers, S. Delalieux, J. Stuckens, W. W. Verstraeten, and P. Coppin, “A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems,” Int. J. Remote Sens. 30(1), 139–147 (2009).
[CrossRef]

Suhaili, A.

H. Z. M. Shafri, A. Suhaili, and S. Mansor, “The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis,” J. Computer Sci. 3(6), 419–423 (2007).
[CrossRef]

Switzer, P.

A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Trans. Geosci. Rem. Sens. 26(1), 65–74 (1988).
[CrossRef]

Swope, S.

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

Tax, D. M. J.

D. M. J. Tax and R. P. W. Duin, “Support vector domain description,” Pattern Recognit. Lett. 20(11-13), 1191–1199 (1999).
[CrossRef]

Tracy, C. R.

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

Valencia, D.

A. Plaza, D. Valencia, J. Plaza, and P. Martinez, “Commodity cluster-based parallel processing of hyperspectral imagery,” J. Parallel Distrib. Comput. 66(3), 345–358 (2006).
[CrossRef]

Vane, G.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ. 44(2–3), 127–143 (1993).
[CrossRef]

Verstraeten, W. W.

B. Somers, S. Delalieux, J. Stuckens, W. W. Verstraeten, and P. Coppin, “A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems,” Int. J. Remote Sens. 30(1), 139–147 (2009).
[CrossRef]

Wang, J.

C. Kwan, B. Ayhan, G. Chen, J. Wang, B. Ji, and C.-I. Chang, “A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents,” IEEE Trans. Geosci. Rem. Sens. 44(2), 409–419 (2006).
[CrossRef]

Wessman, C. A.

C. A. Bateson, G. P. Asner, and C. A. Wessman, “Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis,” IEEE Trans. Geosci. Rem. Sens. 38(2), 1083–1094 (2000).
[CrossRef]

Winter, M. E.

M. E. Winter, “N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data,” Proc. SPIE 3753, 266–275 (1999).
[CrossRef]

Wu, C.-C.

C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, “A new growing method for simplex-based endmember extraction algorithm,” IEEE Trans. Geosci. Rem. Sens. 44(10), 2804–2819 (2006).
[CrossRef]

Zare, A.

A. Zare and P. Gader, “Hyperspectral band selection and endmember detection using sparsity promoting priors,” IEEE Geosci. Remote Sens. Lett. 5(2), 256–260 (2008).
[CrossRef]

Zhang, J.

J. Zhang, B. Rivard, and D. M. Rogge, “The Successive Projection Algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data,” Sensors 8(2), 1321–1342 (2008).
[CrossRef]

Zortea, M.

M. Zortea and A. Plaza, “Spatial preprocessing for endmember extraction,” IEEE Trans. Geosci. Rem. Sens. 47(8), 2679–2693 (2009).
[CrossRef]

IEEE Geosci. Remote Sens. Lett. (1)

A. Zare and P. Gader, “Hyperspectral band selection and endmember detection using sparsity promoting priors,” IEEE Geosci. Remote Sens. Lett. 5(2), 256–260 (2008).
[CrossRef]

IEEE Trans. Geosci. Rem. Sens. (11)

A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Trans. Geosci. Rem. Sens. 26(1), 65–74 (1988).
[CrossRef]

C. Kwan, B. Ayhan, G. Chen, J. Wang, B. Ji, and C.-I. Chang, “A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents,” IEEE Trans. Geosci. Rem. Sens. 44(2), 409–419 (2006).
[CrossRef]

C. A. Bateson, G. P. Asner, and C. A. Wessman, “Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis,” IEEE Trans. Geosci. Rem. Sens. 38(2), 1083–1094 (2000).
[CrossRef]

M. Zortea and A. Plaza, “Spatial preprocessing for endmember extraction,” IEEE Trans. Geosci. Rem. Sens. 47(8), 2679–2693 (2009).
[CrossRef]

A. Plaza and C.-I. Chang, “Impact of initialization on design of endmember extraction algorithms,” IEEE Trans. Geosci. Rem. Sens. 44(11), 3397–3407 (2006).
[CrossRef]

A. M. Filippi and R. Archibald, “Support Vector Machine-Based Endmember Extraction,” IEEE Trans. Geosci. Rem. Sens. 47(3), 771–791 (2009).
[CrossRef]

A. Plaza, P. Martínez, R. Pérez, and J. Plaza, “A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data,” IEEE Trans. Geosci. Rem. Sens. 42(3), 650–663 (2004).
[CrossRef]

M. Brown, H. G. Lewis, and S. R. Gunn, “Linear mixture models and support vector machines for remote sensing,” IEEE Trans. Geosci. Rem. Sens. 38(5), 2346–2360 (2000).
[CrossRef]

C.-I. Chang and Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Rem. Sens. 42(3), 608–619 (2004).
[CrossRef]

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral classification,” IEEE Trans. Geosci. Rem. Sens. 43(6), 1351–1362 (2005).
[CrossRef]

C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, “A new growing method for simplex-based endmember extraction algorithm,” IEEE Trans. Geosci. Rem. Sens. 44(10), 2804–2819 (2006).
[CrossRef]

Int. J. Remote Sens. (3)

E. Ramsey, A. Rangoonwala, G. Nelson, R. Ehrlich, and K. Martella, “Generation and validation of characteristic spectra from EO1 Hyperion image data for detecting the occurrence of the invasive species, Chinese tallow,” Int. J. Remote Sens. 26(8), 1611–1636 (2005).
[CrossRef]

D. R. Peddle and M. Smith, “Spectral mixture analysis of agricultural crops: endmember validation and biophysical estimation in potato plots,” Int. J. Remote Sens. 26(22), 4959–4979 (2005).
[CrossRef]

B. Somers, S. Delalieux, J. Stuckens, W. W. Verstraeten, and P. Coppin, “A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems,” Int. J. Remote Sens. 30(1), 139–147 (2009).
[CrossRef]

J. Computer Sci. (1)

H. Z. M. Shafri, A. Suhaili, and S. Mansor, “The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis,” J. Computer Sci. 3(6), 419–423 (2007).
[CrossRef]

J. Parallel Distrib. Comput. (1)

A. Plaza, D. Valencia, J. Plaza, and P. Martinez, “Commodity cluster-based parallel processing of hyperspectral imagery,” J. Parallel Distrib. Comput. 66(3), 345–358 (2006).
[CrossRef]

Lincoln Laboratory J. (1)

N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Laboratory J. 14(1), 55–78 (2003).

Opt. Eng. (1)

Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D’Amico, “New hyperspectral discrimination measure for spectral characterization,” Opt. Eng. 43(8), 1777–1786 (2004).
[CrossRef]

Opt. Express (1)

Pattern Recognit. Lett. (1)

D. M. J. Tax and R. P. W. Duin, “Support vector domain description,” Pattern Recognit. Lett. 20(11-13), 1191–1199 (1999).
[CrossRef]

Photogramm. Eng. Remote Sensing (1)

E. Ramsey and A. Rangoonwala, “Canopy reflectance related to marsh dieback onset and progression in coastal Louisiana,” Photogramm. Eng. Remote Sensing 72(6), 641–652 (2006).

Proc. SPIE (2)

M. E. Winter, “N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data,” Proc. SPIE 3753, 266–275 (1999).
[CrossRef]

J. Gruninger, A. J. Ratkowski, and M. L. Hoke, “The sequential maximum angle convex cone (SMACC) endmember model,” Proc. SPIE 5425, 1–14 (2004).
[CrossRef]

Remote Sens. Environ. (9)

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ. 44(2–3), 127–143 (1993).
[CrossRef]

X. Miao, P. Gong, S. Swope, R. Pu, R. Carruthers, G. L. Anderson, J. S. Heaton, and C. R. Tracy, “Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear mixture models,” Remote Sens. Environ. 101(3), 329–341 (2006).
[CrossRef]

G. P. Asner, “Biophysical and biochemical sources of variability in canopy reflectance,” Remote Sens. Environ. 64(3), 234–253 (1998).
[CrossRef]

G. P. Asner and D. B. Lobell, “A biogeophysical approach for automated SWIR unmixing of soils and vegetation,” Remote Sens. Environ. 74(1), 99–112 (2000).
[CrossRef]

A. M. Filippi and J. R. Jensen, “Fuzzy learning vector quantization for hyperspectral coastal vegetation classification,” Remote Sens. Environ. 100(4), 512–530 (2006).
[CrossRef]

J. C. Price, “How unique are spectral signatures?” Remote Sens. Environ. 49(3), 181–186 (1994).
[CrossRef]

C. C. Borel and S. A. W. Gerstl, “Nonlinear spectral mixing models for vegetative and soil surfaces,” Remote Sens. Environ. 47(3), 403–416 (1994).
[CrossRef]

F. A. Kruse, A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon, and A. Goetz, “The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data,” Remote Sens. Environ. 44(2–3), 145–163 (1993).
[CrossRef]

R. G. Congalton, “A review of assessing the accuracy of classifications of remotely sensed data,” Remote Sens. Environ. 37(1), 35–46 (1991).
[CrossRef]

Sensors (1)

J. Zhang, B. Rivard, and D. M. Rogge, “The Successive Projection Algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data,” Sensors 8(2), 1321–1342 (2008).
[CrossRef]

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J. Plaza, A. Plaza, P. Martínez, and R. Pérez, “H-COMP: a tool for quantitative and comparative analysis of endmember identification algorithms,” in Proc., Geoscience and Remote Sensing Symposium,2003 (IGARSS, 2003), pp. 291–293.

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Figures (4)

Fig. 1
Fig. 1

Simple two-dimensional example illustration of the robust nature of SVM-BEE. Even with a significant amount of noise for a given distribution, the endmembers are identified accurately. (a) An iterative result of SVM-BEE’s decision surface-generation algorithm [2]. Blue dots are sampled points determined to be support vectors; yellow dots are pseudopoints; and estimated endmembers are shown in red. (b) Depiction of the convex hull that minimizes the distance between the previously-identified support vectors. Final SVM-BEE-estimated endmembers are represented by the red dots, which reflect a bias correction [2].

Fig. 2
Fig. 2

(a) Masked Indian Pines AVIRIS image (band 37, 728.49 nm) and (b) ground-reference map.

Fig. 3
Fig. 3

Spectral endmember plots generated by (a) SVM-BEE Trial 1; (b) N-FINDR Trial 7 (SNR = 600; maxEM = 20); and (c) N-FINDR Trial 19 (SNR = 1400; maxEM = 20).

Fig. 4
Fig. 4

Spectral endmember plots generated by SMACC Trial 5.

Tables (6)

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Table 1 Number of endmembers extracted (NEE) from the Indian Pines AVIRIS image by reference map class, for each endmember-extraction algorithm (EEA) trial evaluated. A dash (–) indicates that a given endmember was not extracted.

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Table 2 Spectral angle mapper (SAM) angle (in radians) and spectral information divergence (SID) between each SVM-BEE-estimated endmember from replicate Trials 2 and 3 and the corresponding base/reference spectrum from SVM-BEE Trial 1, computed based on AVIRIS radiance data. Entries in bold indicate instances where there were actually no spectral differences between the replicate endmember and the Trial 1 base endmember (i.e., they were identical), even though non-zero values were computed by SAM or SID.

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Table 3 SAM overall classificaton accuracy based on SVM-BEE (Trial 1) and N-FINDR (Trials 7 and 19) endmembers for five and six classes a

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Table 4 Results of the Kappa analysis for comparison between error matrices for SAM classifications based on SVM-BEE (Trial 1) and N-FINDR (Trials 7 and 19) endmembers (six classes), with two N-FINDR-based endmember/classification treatments a,b

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Table 5 SAM overall classification accuracy based on SVM-BEE (Trial 1) and SMACC (Trial 5) endmembers for nine classes, including SMACC-based SAM results where both 9 endmembers and 16 initial endmembers were used, respectively

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Table 6 Results of the Kappa analysis for comparison between error matrices for SAM classifications based on SVM-BEE (Trial 1) and SMACC (Trial 5) endmembers (nine classes), with two SMACC-based endmember/classification treatments a,b

Equations (3)

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S o u t = { p j + μ i D f ( p j ) }
S A M ( s i , s j ) = cos 1 ( s i , s j s i s j ) ,
K ^ = N i = 1 r x i i i = 1 r ( x i + x + i ) / N 2 i = 1 r ( x i + x + i ) ,

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