Abstract

Through retrofitting the descriptor of a scale-invariant feature transform (SIFT) and developing a new similarity measure function based on trajectories generated from Lissajous curves, a new remote sensing image registration approach is constructed, which is more robust and accurate than prior approaches. In complex cases where the correct rate of feature matching is below 20%, the retrofitted SIFT descriptor improves the correct rate to nearly 100%. Mostly, the similarity measure function makes it possible to quantitatively analyze the temporary change of the same geographic position.

© 2010 OSA

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References

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  1. A. Wade and F. Fitzke, “A fast, robust pattern recognition asystem for low light level image registration and its application to retinal imaging,” Opt. Express 3(5), 190–197 (1998).
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    [CrossRef]
  3. Z. Li, Z. Bao, H. Li, and G. Liao, “Image autocoregistration and InSAR interferogram estimation using joint subspace projection,” IEEE Trans. Geosci. Rem. Sens. 44(2), 288–297 (2006).
    [CrossRef]
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  5. Z. Cao, Y. Zheng, Y. Wang, and R. Yan, “An algorithm for object function optimization in mutual information-based image registration,” Proceedings of the 2008 Congress on Image and Signal Processing, 4, 426–430 (2008).
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  7. A. Wong and J. Orchard, “Efficient FFT-accelerated approach to invariant optical-LIDAR registration,” IEEE Trans. Geosci. Rem. Sens. 463917–3925 (2008).
    [CrossRef]
  8. I. Zavorin and J. Le Moigne, “Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery,” IEEE Trans. Image Process. 14(6), 770–782 (2005).
    [CrossRef] [PubMed]
  9. J. G. Liu and H. Yan, “Phase correlation pixel-to-pixel image co-registration based on optical flow and median shift propagation,” Int. J. Remote Sens. 29(20), 5943–5956 (2008).
    [CrossRef]
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    [CrossRef] [PubMed]
  12. B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21(11), 977–1000 (2003).
    [CrossRef]
  13. A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
    [CrossRef] [PubMed]
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    [CrossRef]
  15. K. Mikolajczyk and C. Schmid, “Scale and affine invariant interest point detectors,” Int. J. Comput. Vis. 60(1), 63–86 (2004).
    [CrossRef]
  16. T. Tuytelaars and L. Van Gool, “Matching widely separated views based on affinely invariant neighborhoods,” Int. J. Comput. Vis. 59(1), 61–85 (2004).
    [CrossRef]
  17. F. P. Nava, and A. P. Nava, “A probabilistic generative model for unsupervised invariant change detection in remote sensing images,” in IEEE International Geoscience and Remote Sensing Symposium (Barcelona, 2007), pages 2362–2365.
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    [CrossRef] [PubMed]
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  20. N. Netanyahu, J. Le Moigne, and J. Masek, “Georegistration of Landsat data via robust matching of multiresolution features,” IEEE Trans. Geosci. Rem. Sens. 42(7), 1586–1600 (2004).
    [CrossRef]
  21. A. Wong and D. A. Clausi, “ARRSI: Automatic registration of remote-sensing images,” IEEE Trans. Geosci. Rem. Sens. 45(5), 1483–1493 (2007).
    [CrossRef]
  22. C. Stewart, “Robust parameter estimation in computer vision,” SIAM Rev. 41(3), 513–537 (1999).
    [CrossRef]
  23. M. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
    [CrossRef]
  24. N. Alajlan, I. El Rube, M. S. Kamel, and G. Freeman, “Shape retrieval using triangle-area representation and dynamic space warping,” Pattern Recognit. 40(7), 1911–1920 (2007).
    [CrossRef]
  25. N. Kyriakoulis, A. Gasteratos, and S. G. Mouroutsos, “Fuzzy vergence control for an active binocular vision system,” in 7th International Conference on Cybernetic Intelligent Systems (London, 2008), pages 1–5.
  26. A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
    [CrossRef] [PubMed]
  27. http://bigwww.epfl.ch/thevenaz/turboreg/ .
  28. http://www.mathworks.com/ .
  29. http://vision.ece.ucsb.edu/registration/demo/ .

2009 (2)

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

2008 (3)

A. Wong and J. Orchard, “Efficient FFT-accelerated approach to invariant optical-LIDAR registration,” IEEE Trans. Geosci. Rem. Sens. 463917–3925 (2008).
[CrossRef]

J. G. Liu and H. Yan, “Phase correlation pixel-to-pixel image co-registration based on optical flow and median shift propagation,” Int. J. Remote Sens. 29(20), 5943–5956 (2008).
[CrossRef]

G. Hong and Y. Zhang, “Wavelet-based image registration technique for high-resolution remote sensing images,” Comput. Geosci. 34(12), 1708–1720 (2008).
[CrossRef]

2007 (4)

S. Guyot, M. Anastasiadou, E. Deléchelle, and A. De Martino, “Registration scheme suitable to Mueller matrix imaging for biomedical applications,” Opt. Express 15(12), 7393–7400 (2007).
[CrossRef] [PubMed]

J. Orchard, “Efficient least squares multimodal registration with a globally exhaustive alignment search,” IEEE Trans. Image Process. 16(10), 2526–2534 (2007).
[CrossRef] [PubMed]

N. Alajlan, I. El Rube, M. S. Kamel, and G. Freeman, “Shape retrieval using triangle-area representation and dynamic space warping,” Pattern Recognit. 40(7), 1911–1920 (2007).
[CrossRef]

A. Wong and D. A. Clausi, “ARRSI: Automatic registration of remote-sensing images,” IEEE Trans. Geosci. Rem. Sens. 45(5), 1483–1493 (2007).
[CrossRef]

2006 (2)

Z. Li, Z. Bao, H. Li, and G. Liao, “Image autocoregistration and InSAR interferogram estimation using joint subspace projection,” IEEE Trans. Geosci. Rem. Sens. 44(2), 288–297 (2006).
[CrossRef]

S. Jiao, C. Wu, R. W. Knighton, G. Gregori, and C. A. Puliafito, “Registration of high-density cross sectional images to the fundus image in spectral-domain ophthalmic optical coherence tomography,” Opt. Express 14(8), 3368–3376 (2006).
[CrossRef] [PubMed]

2005 (1)

I. Zavorin and J. Le Moigne, “Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery,” IEEE Trans. Image Process. 14(6), 770–782 (2005).
[CrossRef] [PubMed]

2004 (4)

N. Netanyahu, J. Le Moigne, and J. Masek, “Georegistration of Landsat data via robust matching of multiresolution features,” IEEE Trans. Geosci. Rem. Sens. 42(7), 1586–1600 (2004).
[CrossRef]

D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004).
[CrossRef]

K. Mikolajczyk and C. Schmid, “Scale and affine invariant interest point detectors,” Int. J. Comput. Vis. 60(1), 63–86 (2004).
[CrossRef]

T. Tuytelaars and L. Van Gool, “Matching widely separated views based on affinely invariant neighborhoods,” Int. J. Comput. Vis. 59(1), 61–85 (2004).
[CrossRef]

2003 (2)

H. Chen, M. K. Arora, and P. K. Varshney, “Mutual information-based image registration for remote sensing data,” Int. J. Remote Sens. 24(18), 3701–3706 (2003).
[CrossRef]

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21(11), 977–1000 (2003).
[CrossRef]

1999 (1)

C. Stewart, “Robust parameter estimation in computer vision,” SIAM Rev. 41(3), 513–537 (1999).
[CrossRef]

1998 (1)

1981 (1)

M. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[CrossRef]

Alajlan, N.

N. Alajlan, I. El Rube, M. S. Kamel, and G. Freeman, “Shape retrieval using triangle-area representation and dynamic space warping,” Pattern Recognit. 40(7), 1911–1920 (2007).
[CrossRef]

Anastasiadou, M.

Arora, M. K.

H. Chen, M. K. Arora, and P. K. Varshney, “Mutual information-based image registration for remote sensing data,” Int. J. Remote Sens. 24(18), 3701–3706 (2003).
[CrossRef]

Banerjee, A.

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

Bao, Z.

Z. Li, Z. Bao, H. Li, and G. Liao, “Image autocoregistration and InSAR interferogram estimation using joint subspace projection,” IEEE Trans. Geosci. Rem. Sens. 44(2), 288–297 (2006).
[CrossRef]

Bolles, R. C.

M. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[CrossRef]

Chen, H.

H. Chen, M. K. Arora, and P. K. Varshney, “Mutual information-based image registration for remote sensing data,” Int. J. Remote Sens. 24(18), 3701–3706 (2003).
[CrossRef]

Clausi, D. A.

A. Wong and D. A. Clausi, “ARRSI: Automatic registration of remote-sensing images,” IEEE Trans. Geosci. Rem. Sens. 45(5), 1483–1493 (2007).
[CrossRef]

De Martino, A.

Deléchelle, E.

El Rube, I.

N. Alajlan, I. El Rube, M. S. Kamel, and G. Freeman, “Shape retrieval using triangle-area representation and dynamic space warping,” Pattern Recognit. 40(7), 1911–1920 (2007).
[CrossRef]

Fischler, M.

M. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[CrossRef]

Fitzke, F.

Flusser, J.

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21(11), 977–1000 (2003).
[CrossRef]

Freeman, G.

N. Alajlan, I. El Rube, M. S. Kamel, and G. Freeman, “Shape retrieval using triangle-area representation and dynamic space warping,” Pattern Recognit. 40(7), 1911–1920 (2007).
[CrossRef]

Gregori, G.

Guyot, S.

Hong, G.

G. Hong and Y. Zhang, “Wavelet-based image registration technique for high-resolution remote sensing images,” Comput. Geosci. 34(12), 1708–1720 (2008).
[CrossRef]

Jiao, S.

Kamel, M. S.

N. Alajlan, I. El Rube, M. S. Kamel, and G. Freeman, “Shape retrieval using triangle-area representation and dynamic space warping,” Pattern Recognit. 40(7), 1911–1920 (2007).
[CrossRef]

Knighton, R. W.

Le Moigne, J.

I. Zavorin and J. Le Moigne, “Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery,” IEEE Trans. Image Process. 14(6), 770–782 (2005).
[CrossRef] [PubMed]

N. Netanyahu, J. Le Moigne, and J. Masek, “Georegistration of Landsat data via robust matching of multiresolution features,” IEEE Trans. Geosci. Rem. Sens. 42(7), 1586–1600 (2004).
[CrossRef]

Li, H.

Z. Li, Z. Bao, H. Li, and G. Liao, “Image autocoregistration and InSAR interferogram estimation using joint subspace projection,” IEEE Trans. Geosci. Rem. Sens. 44(2), 288–297 (2006).
[CrossRef]

Li, Z.

Z. Li, Z. Bao, H. Li, and G. Liao, “Image autocoregistration and InSAR interferogram estimation using joint subspace projection,” IEEE Trans. Geosci. Rem. Sens. 44(2), 288–297 (2006).
[CrossRef]

Liao, G.

Z. Li, Z. Bao, H. Li, and G. Liao, “Image autocoregistration and InSAR interferogram estimation using joint subspace projection,” IEEE Trans. Geosci. Rem. Sens. 44(2), 288–297 (2006).
[CrossRef]

Liu, J. G.

J. G. Liu and H. Yan, “Phase correlation pixel-to-pixel image co-registration based on optical flow and median shift propagation,” Int. J. Remote Sens. 29(20), 5943–5956 (2008).
[CrossRef]

Lowe, D.

D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004).
[CrossRef]

Masek, J.

N. Netanyahu, J. Le Moigne, and J. Masek, “Georegistration of Landsat data via robust matching of multiresolution features,” IEEE Trans. Geosci. Rem. Sens. 42(7), 1586–1600 (2004).
[CrossRef]

Mikolajczyk, K.

K. Mikolajczyk and C. Schmid, “Scale and affine invariant interest point detectors,” Int. J. Comput. Vis. 60(1), 63–86 (2004).
[CrossRef]

Netanyahu, N.

N. Netanyahu, J. Le Moigne, and J. Masek, “Georegistration of Landsat data via robust matching of multiresolution features,” IEEE Trans. Geosci. Rem. Sens. 42(7), 1586–1600 (2004).
[CrossRef]

Orchard, J.

A. Wong and J. Orchard, “Efficient FFT-accelerated approach to invariant optical-LIDAR registration,” IEEE Trans. Geosci. Rem. Sens. 463917–3925 (2008).
[CrossRef]

J. Orchard, “Efficient least squares multimodal registration with a globally exhaustive alignment search,” IEEE Trans. Image Process. 16(10), 2526–2534 (2007).
[CrossRef] [PubMed]

Puliafito, C. A.

Rajwade, A.

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

Rangarajan, A.

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

Schmid, C.

K. Mikolajczyk and C. Schmid, “Scale and affine invariant interest point detectors,” Int. J. Comput. Vis. 60(1), 63–86 (2004).
[CrossRef]

Stewart, C.

C. Stewart, “Robust parameter estimation in computer vision,” SIAM Rev. 41(3), 513–537 (1999).
[CrossRef]

Tuytelaars, T.

T. Tuytelaars and L. Van Gool, “Matching widely separated views based on affinely invariant neighborhoods,” Int. J. Comput. Vis. 59(1), 61–85 (2004).
[CrossRef]

Van Gool, L.

T. Tuytelaars and L. Van Gool, “Matching widely separated views based on affinely invariant neighborhoods,” Int. J. Comput. Vis. 59(1), 61–85 (2004).
[CrossRef]

Varshney, P. K.

H. Chen, M. K. Arora, and P. K. Varshney, “Mutual information-based image registration for remote sensing data,” Int. J. Remote Sens. 24(18), 3701–3706 (2003).
[CrossRef]

Wade, A.

Wong, A.

A. Wong and J. Orchard, “Efficient FFT-accelerated approach to invariant optical-LIDAR registration,” IEEE Trans. Geosci. Rem. Sens. 463917–3925 (2008).
[CrossRef]

A. Wong and D. A. Clausi, “ARRSI: Automatic registration of remote-sensing images,” IEEE Trans. Geosci. Rem. Sens. 45(5), 1483–1493 (2007).
[CrossRef]

Wu, C.

Yan, H.

J. G. Liu and H. Yan, “Phase correlation pixel-to-pixel image co-registration based on optical flow and median shift propagation,” Int. J. Remote Sens. 29(20), 5943–5956 (2008).
[CrossRef]

Zavorin, I.

I. Zavorin and J. Le Moigne, “Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery,” IEEE Trans. Image Process. 14(6), 770–782 (2005).
[CrossRef] [PubMed]

Zhang, Y.

G. Hong and Y. Zhang, “Wavelet-based image registration technique for high-resolution remote sensing images,” Comput. Geosci. 34(12), 1708–1720 (2008).
[CrossRef]

Zitova, B.

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21(11), 977–1000 (2003).
[CrossRef]

Commun. ACM (1)

M. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[CrossRef]

Comput. Geosci. (1)

G. Hong and Y. Zhang, “Wavelet-based image registration technique for high-resolution remote sensing images,” Comput. Geosci. 34(12), 1708–1720 (2008).
[CrossRef]

IEEE Trans. Geosci. Rem. Sens. (4)

Z. Li, Z. Bao, H. Li, and G. Liao, “Image autocoregistration and InSAR interferogram estimation using joint subspace projection,” IEEE Trans. Geosci. Rem. Sens. 44(2), 288–297 (2006).
[CrossRef]

A. Wong and J. Orchard, “Efficient FFT-accelerated approach to invariant optical-LIDAR registration,” IEEE Trans. Geosci. Rem. Sens. 463917–3925 (2008).
[CrossRef]

N. Netanyahu, J. Le Moigne, and J. Masek, “Georegistration of Landsat data via robust matching of multiresolution features,” IEEE Trans. Geosci. Rem. Sens. 42(7), 1586–1600 (2004).
[CrossRef]

A. Wong and D. A. Clausi, “ARRSI: Automatic registration of remote-sensing images,” IEEE Trans. Geosci. Rem. Sens. 45(5), 1483–1493 (2007).
[CrossRef]

IEEE Trans. Image Process. (2)

I. Zavorin and J. Le Moigne, “Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery,” IEEE Trans. Image Process. 14(6), 770–782 (2005).
[CrossRef] [PubMed]

J. Orchard, “Efficient least squares multimodal registration with a globally exhaustive alignment search,” IEEE Trans. Image Process. 16(10), 2526–2534 (2007).
[CrossRef] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

A. Rajwade, A. Banerjee, and A. Rangarajan, “Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration,” IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 475–491 (2009).
[CrossRef] [PubMed]

Image Vis. Comput. (1)

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21(11), 977–1000 (2003).
[CrossRef]

Int. J. Comput. Vis. (3)

D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004).
[CrossRef]

K. Mikolajczyk and C. Schmid, “Scale and affine invariant interest point detectors,” Int. J. Comput. Vis. 60(1), 63–86 (2004).
[CrossRef]

T. Tuytelaars and L. Van Gool, “Matching widely separated views based on affinely invariant neighborhoods,” Int. J. Comput. Vis. 59(1), 61–85 (2004).
[CrossRef]

Int. J. Remote Sens. (2)

J. G. Liu and H. Yan, “Phase correlation pixel-to-pixel image co-registration based on optical flow and median shift propagation,” Int. J. Remote Sens. 29(20), 5943–5956 (2008).
[CrossRef]

H. Chen, M. K. Arora, and P. K. Varshney, “Mutual information-based image registration for remote sensing data,” Int. J. Remote Sens. 24(18), 3701–3706 (2003).
[CrossRef]

Opt. Express (3)

Pattern Recognit. (1)

N. Alajlan, I. El Rube, M. S. Kamel, and G. Freeman, “Shape retrieval using triangle-area representation and dynamic space warping,” Pattern Recognit. 40(7), 1911–1920 (2007).
[CrossRef]

SIAM Rev. (1)

C. Stewart, “Robust parameter estimation in computer vision,” SIAM Rev. 41(3), 513–537 (1999).
[CrossRef]

Other (8)

N. Kyriakoulis, A. Gasteratos, and S. G. Mouroutsos, “Fuzzy vergence control for an active binocular vision system,” in 7th International Conference on Cybernetic Intelligent Systems (London, 2008), pages 1–5.

http://bigwww.epfl.ch/thevenaz/turboreg/ .

http://www.mathworks.com/ .

http://vision.ece.ucsb.edu/registration/demo/ .

F. Eugenio, F. Marques, and J. Marcello, A contour-based approach to automatic and accurate registration of multitemporal and multisensor satellite imagery,” in IEEE International Geoscience and Remote Sensing Symposium, Volume 6 (Toronto, 2002), pages 3390–3392.

F. P. Nava, and A. P. Nava, “A probabilistic generative model for unsupervised invariant change detection in remote sensing images,” in IEEE International Geoscience and Remote Sensing Symposium (Barcelona, 2007), pages 2362–2365.

Z. Cao, Y. Zheng, Y. Wang, and R. Yan, “An algorithm for object function optimization in mutual information-based image registration,” Proceedings of the 2008 Congress on Image and Signal Processing, 4, 426–430 (2008).

G. Shao, F. Yao, and M. Malkani, “Aerial image registration based on joint feature-spatial spaces, curve and template matching,” in IEEE International Conference on Information and Automation (Hunan, China, 2008), pages 863–868.

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Figures (8)

Fig. 1
Fig. 1

Feature points and edges in reference and template images. (a) Feature point and one edge around it in the reference image. (b) Feature point and one edge around it in the template image. (c) Corresponding triangle of one matched point from (a). (d) Corresponding triangle of one matched point from (b).

Fig. 2
Fig. 2

TAR image and its applications. (a) 3D picture of TAR signatures of edges in the reference image. (b) 3D picture of TAR signatures of edges in the template image.

Fig. 3
Fig. 3

Sets of images. (a) First reference image. (b) First template image. (c) Part of the reference image. (d) Part of the template image.

Fig. 4
Fig. 4

Sets of images. (a) Second reference image. (b) Second template image. (c) Third reference image. (d) Third template image.

Fig. 5
Fig. 5

The correct matching rate of the 500 top pairs from the queue of matched feature points with the SIFT and retrofitted SIFT algorithms, respectively.

Fig. 6
Fig. 6

Matching point and 3D plot of the similarity matrix. (a) The white cross is the real matching point, and the green cross is the matching point found with ZNCC. (b) 3D plot of the ZNCC similarity matrix. (c) The white cross is the real matching point, and the green cross is the matching point found with MI. (d) 3D plot of the similarity matrix calculated with MI.

Fig. 7
Fig. 7

Matching point and 3D plot of the similarity matrix. (a) The white cross is the real matching point, overlapped by the green cross, which is the matching point found with MILF. (b) 3D plots of the MILF similarity matrix.

Fig. 8
Fig. 8

Mosaic result of the remotely sensing images.

Tables (1)

Tables Icon

Table 1 Details of sets of test remote sensing images from USGS and NASA/JPL.

Equations (10)

Equations on this page are rendered with MathJax. Learn more.

T A R ( p b , p m , p e ) = 1 2 | x b y b 1 x m y m 1 x e y e 1 | = 1 2 ( x b y m + x m y e + x e y b x e y m x b y e x m y b ) ,
( x ^ y ^ ) = ( a b c d ) ( x y ) + ( e f ) ,
T A ^ R ( p ^ b , p ^ m , p ^ e ) = ( a d b c ) T A R ( p b , p m , p e ) .
ImR [ i 1 ] [ i 2 ] = TAR ( p r i 1 , f r , p r i 2 ) ImS [ j 1 ] [ j 2 ] = TAR ( p s j 1 , f s , p s j 2 ) ,
{ x = A x sin ( ω x t + ϕ x ) y = A y sin ( ω y t + ϕ y ) .
MILF ( p R , p S ) = λ ( G 1 R , G 2 R , G 1 S , G 2 S ) ,
MILF ( p R , p S ) = H ( G 1 R , G 2 R ) + H ( G 1 S , G 2 S ) H ( G 1 R , G 2 R , G 1 S , G 2 S ) ,  
H ( G 1 R , G 2 R ) = r 1 G 1 R r 2 G 2 R p ( r 1 , r 2 ) log p ( r 1 , r 2 ) , H ( G 1 S , G 2 S ) = s 1 G 1 S s 2 G 2 S p ( s 1 , s 2 ) log p ( s 1 , s 2 )
H ( G 1 R , G 2 R , G 1 S , G 2 S ) = r 1 G 1 R r 2 G 2 R s 1 G 1 S s 2 G 2 S p ( r 1 , r 2 , s 1 , s 2 ) log p ( r 1 , r 2 , s 1 , s 2 ) .
( x ' y ' z ' ) = ( a 11 a 12 a 13 a 21 a 22 a 23 a 31 a 32 a 33 ) ( x y 1 ) .

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