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2018-09-16
Coarse-to-Fine Accurate Registration for Airborne SAR Images Using SAR-Fast and DSP-LATCH
By
Progress In Electromagnetics Research, Vol. 163, 89-106, 2018
Abstract
Synthetic Aperture Radar (SAR) image registration is to establish reliable correspondences among the images of the same scene. It is a challenging problem to register the airborne SAR images for the instability of airborne SAR systems and the lack of appropriate geo-reference data. Besides, techniques for registering satellite-based SAR images relying on rigorous SAR geocoding cannot be directly applied to airborne SAR images. To address this problem, we present a coarse-to-fine registration method for airborne SAR images by combining SAR-FAST (Features from Accelerated Segment Test) feature detector and DSP-LATCH (Domain-Size Pooling of Learned Arrangements of Three patCH) feature descriptor, which only relies on the gray level intensity of SAR data. More precisely, we first apply SAR-FAST, which is an adapted version of FAST for analyzing SAR images, to detect corners with high accuracy and low computational complexity. To reduce the disturbance of speckle noise as well as to achieve efficient and discriminative feature description, we further propose an improved descriptor named DSP-LATCH to describe the features, which combines the Domain-size Pooling scheme of DSP-SIFT (Scale-Invariant Feature Transform) and the idea of comparing triplets of patches rather than individual pixel values of LATCH. Finally, we conduct a coarse-to-fine strategy for SAR image registration by employing binary feature matching and the Powell algorithm. Compared with the existing feature based SAR image registration methods, e.g., SIFT and its variants, our method yields more reliable matched feature points and achieves higher registration accuracy. The experimental results on different scenes of airborne SAR images demonstrate the superiority of the proposed method in terms of robustness and accuracy.
Citation
Huai Yu Wen Yang Yan Liu , "Coarse-to-Fine Accurate Registration for Airborne SAR Images Using SAR-Fast and DSP-LATCH," Progress In Electromagnetics Research, Vol. 163, 89-106, 2018.
doi:10.2528/PIER18070801
http://www.jpier.org/PIER/pier.php?paper=18070801
References

1. Xiang, Y., F. Wang, and H. You, "OS-SIFT: A robust SIFT-like algorithm for high-resolution optical-to-SAR image registration in suburban areas," IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 6, 3078-3090, 2018.
doi:10.1109/TGRS.2018.2790483

2. Marin, C., F. Bovolo, and L. Bruzzone, "Building change detection in multitemporal very high resolution SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 5, 2664-2682, 2015.
doi:10.1109/TGRS.2014.2363548

3. Hong, Z., et al., "A new image registration method for multi-frequency airborne high-resolution SAR images," IEEE International Geoscience and Remote Sensing Symposium, 167-169, 2003.

4. Gao, G., X. Qin, and S. Zhou, "Modeling SAR images based on a generalized gamma distribution for texture component," Progress In Electromagnetics Research, Vol. 137, 669-685, 2013.
doi:10.2528/PIER13011807

5. Wang, F., H. You, and X. Fu, "Adapted anisotropic Gaussian SIFT matching strategy for SAR registration," IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 1, 160-164, 2015.
doi:10.1109/LGRS.2014.2330593

6. Lowe, D. G., "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, Vol. 60, No. 2, 91-110, 2004.
doi:10.1023/B:VISI.0000029664.99615.94

7. Lindeberg, T., "Scale-space theory: A basic tool for analyzing structures at different scales," Journal of Applied Statistics, Vol. 21, No. 1-2, 225-270, 1994.
doi:10.1080/757582976

8. Datcu, M., "Wavelet-based despeckling of SAR images using Gauss-Markov random fields," IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 12, 4127-4143, 2007.
doi:10.1109/TGRS.2007.906093

9. Yan, T., W. Yang, X. Yang, C. Lopez-Matinez, H. C. Li, and M. Liao, "Polarimetric SAR despeckling by integrating stochastic sampling and contextual patch dissimilarity exploration," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, No. 6, 2738-2753, 2017.
doi:10.1109/JSTARS.2017.2706500

10. Schwind, P., S. Suri, P. Reinartz, and A. Siebert, "Applicability of the SIFT operator to geometric SAR image registration," Remote Sensing, Vol. 31, No. 8, 1959-1980, 2010.
doi:10.1080/01431160902927622

11. Wang, B., J. Zhang, L. Lu, G. Huang, and Z. Zhao, "A uniform SIFT-like algorithm for SAR image registration," IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 7, 1426-1430, 2015.
doi:10.1109/LGRS.2015.2406336

12. Dellinger, F., J. Delon, Y. Gousseau, J. Michel, and F. Tupin, "SAR-SIFT: A SIFT-like algorithm for SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 1, 453-466, 2015.
doi:10.1109/TGRS.2014.2323552

13. Harris, C. and M. Stephens, "A combined corner and edge detector," Alvey Vision Conference, Vol. 3, 147-152, 1988.

14. Liu, Y., H. Yu, W. Yang, and L. Li, "SAR image registration using SAR-FAST corner detection," Journal of Electronics and Information Technology, Vol. 39, No. 2, 430-436, 2017.

15. Zhang, Q., X. Shen, L. Xu, and J. Jia, "Rolling guidance filter," European Conference on Computer Vision, 815-830, 2014.

16. Bay, H., T. Tuytelaars, and L. van Gool, "SURF: Speeded up robust features," European Conference on Computer Vision, 404-417, 2006.

17. Dubois, C., A. Nascetti, A. Thiele, M. Crespi, and S. Hinz, "SAR-SIFT for matching multiple SAR images and radargrammetry," PFG --- Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Vol. 85, No. 3, 149-158, 2017.
doi:10.1007/s41064-017-0019-y

18. Liu, X., Z. Tian, Q. Lu, L. Yang, and C. Chai, "A new affine invariant descriptor framework in shearlets domain for SAR image multiscale registration," AEU --- International Journal of Electronics and Communications, Vol. 67, No. 9, 743-753, 2013.
doi:10.1016/j.aeue.2013.03.002

19. Fan, B., C. Huo, C. Pan, and Q. Kong, "Registration of optical and SAR satellite images by exploring the spatial relationship of the improved SIFT ," IEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 4, 657-661, 2013.
doi:10.1109/LGRS.2012.2216500

20. Dong, J. and S. Soatto, "Domain-size pooling in local descriptors: DSP-SIFT," IEEE Conference on Computer Vision and Pattern Recognition, 5097-5106, 2015.

21. Ye, Y., J. Shan, L. Bruzzone, and L. Shen, "Robust registration of multi-modal remote sensing images based on structural similarity," IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 5, 2941-2958, 2017.
doi:10.1109/TGRS.2017.2656380

22. Rui, J., C. Wang, H. Zhang, and F. Jin, "Multi-sensor SAR image registration based on object shape," Remote Sensing, Vol. 8, 923, 2016.
doi:10.3390/rs8110923

23. Xiang, Y., F. Wang, L. Wan, and H. You, "An advanced rotation invariant descriptor for SAR image registration," Remote Sensing, Vol. 9, 686, 2017.
doi:10.3390/rs9070686

24. Calonder, M., V. Lepetit, C. Strecha, and P. Fua, "BRIEF: Binary robust independent elementary features," European Conference on Computer Vision, 778-792, 2010.

25. Leutenegger, S., M. Chli, and R. Y. Siegwart, "BRISK: Binary robust invariant scalable keypoints," IEEE International Conference on Computer Vision, 2548-2555, 2011.

26. Alahi, A., R. Ortiz, and P. Vandergheynst, "FREAK: Fast retina keypoint," IEEE Conference on Computer Vision and Pattern Recognition, 510-517, 2012.

27. Levi, G. and T. Hassner, "LATCH: Learned arrangements of three patch codes," IEEE Winter Conference on Applications of Computer Vision, 1-9, 2016.

28. Chui, H. and A. Rangarajan, "A new point matching algorithm for non-rigid registration," Computer Vision and Image Understanding, Vol. 89, No. 2, 114-141, 2003.
doi:10.1016/S1077-3142(03)00009-2

29. Liu, S., G. Sun, Z. Niu, N. Li, and Z. Chen, "Robust rigid coherent point drift algorithm based on outlier suppression and its application in image matching," Journal of Applied Remote Sensing, Vol. 9, 095085, 2005.

30. Zhang, H., W. Ni, W. Yan, J. Wu, and S. Li, "Robust SAR image registration based on edge matching and refined coherent point drift," EEE Geoscience and Remote Sensing Letters, Vol. 12, No. 10, 2115-2119, 2015.
doi:10.1109/LGRS.2015.2451396

31. Zhao, M., B. An, Y. Wu, B. Chen, and S. Sun, "A robust delaunay triangulation matching for multispectral/multidate remote sensing image registration," IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 4, 711-715, 2015.
doi:10.1109/LGRS.2014.2359518

32. Pluim, J. P., J. A. Maintz, and M. A. Viergever, "Mutual-information-based registration of medical images: A survey," IEEE Transactions on Medical Imaging, Vol. 22, No. 8, 986-1004, 2003.
doi:10.1109/TMI.2003.815867

33. Powell, M. J. D., "An efficient method for finding the minimum of a function of several variables without calculating derivatives," The Computer Journal, Vol. 7, No. 2, 155-162, 1964.
doi:10.1093/comjnl/7.2.155

34. Liu, G., W. Yang, G.-S. Xia, and M. Liao, "Structure preserving SAR image despeckling via L0-minimization," Progress In Electromagnetics Research, Vol. 141, 347-367, 2013.

35. Walessa, M. and M. Datcu, "Model-based despeckling and information extraction from SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 5, 2258-2269, 2015.
doi:10.1109/36.868883

36. Fan, J., Y. Wu, M. Li, W. Liang, and Q. Zhang, "SAR image registration using multiscale image patch features with sparse representation," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, No. 4, 1483-1493, 2017.
doi:10.1109/JSTARS.2016.2628911

37. Rosten, E., R. Porter, and T. Drummond, "Faster and better: A machine learning approach to corner detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 1, 105-119, 2010.
doi:10.1109/TPAMI.2008.275

38. Rublee, E., V. Rabaud, K. Konolige, and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," IEEE International Conference on Computer Vision, 2564-2571, 2011.

39. Brown, M., G. Hua, and S. Winder, "Discriminative learning of local image descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 1, 43-57, 2011.
doi:10.1109/TPAMI.2010.54

40. Cover, T. M. and J. A. Thomas, Elements of Information Theory, Wiley, New York, 2006.

41. Gong, M., S. Zhao, L. Jiao, D. Tian, and S. Wang, "A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 7, 4328-4338, 2014.
doi:10.1109/TGRS.2013.2281391