As the basis of change detection and image fusion, SAR image registration plays an important role in the interpretation of multi-temporal SAR images. This paper presents a method of SAR image registration based on corner detection using SAR-FAST, which is a customized version of Features from Accelerated Segment Test (FAST) for processing SAR images. The proposed method firstly employs rolling guidance filter to suppress speckle noise. Secondly, the candidate corner point is determined by quantitative analysis of the dissimilarities of the detection windows on the extended circle and the center window. Finally, the error detections are removed by analyzing the intensity distribution properties of the candidate corners. The experimental results show that SAR-FAST can detect a sufficient number of corners with stability and high repeatability, and when applying to image registration, it also can get better registration results.
刘妍,余淮,杨文,李立. 利用SAR-FAST角点检测的合成孔径雷达图像配准方法[J]. 电子与信息学报, 2017, 39(2): 430-436.
LIU Yan, YU Huai, YANG Wen, LI Li. SAR Image Registration Using SAR-FAST Corner Detection. JEIT, 2017, 39(2): 430-436.
WANG Guoli, ZHOU Wei, CHAI Yong, et al. SAR image registration based on monogenic signal theory[J]. Journal of Electronics & Information Technology, 2013, 35(8): 1779-1785. doi: 10.3724/SP.J.1146.2012.01487.
[2]
DALIMIYA C and DHARUN V. A survey of registration techniques in remote sensing images[J]. Indian Journal of Science and Technology, 2015, 26(8). Paper No. 24, doi: 10.17485/ijst/ 2015/v8i26/81048.
[3]
ZHANG H, NI W, YAN W, et al. Robust SAR image registration based on edge matching and refined coherent point drift[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(10): 2115-2119. doi: 10.1109/LGRS.2015.2451396.
LI Yingjie, ZHANG Junju, CHANG Benkang, et al. Joint image registration and fusion for multispectral infrared images[J]. Journal of Electronics & Information Technology, 2016, 38(1): 8-14. doi: 10.11999/JEIT150479.
DENG Liang, SHI Yikai, and ZHANG Juntian. A constrained imaging and registration scheme based on time-varying anatomical priors [J]. Journal of Electronics & Information Technology, 2013, 35(12): 2942-2947. doi: 10.3724/SP.J.1146.2012.01565.
[6]
WANG S, YOU H, and FU K. BFSIFT: A novel method to find feature matches for SAR image registration[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4): 649-653. doi: 10.1109/LGRS.2011.2177437.
[7]
LOWE D. Object recognition from local scale-invariant features[C]. IEEE International Conference on Computer Vision, Corfu, Greece, 1999: 1150-1157.
[8]
SCHWIND P, SURI S, and REINARTZ P. Applicability of the SIFT operator to geometric SAR image registration[J]. International Journal of Remote Sensing, 2010, 31(8): 1959-1980. doi: 10.1080/01431160902927622.
[9]
WANG B, ZHANG J, LU L, et al. A uniform SIFT-like algorithm for SAR image registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 12(7): 1426-1430. doi: 10.1109/LGRS.2015.2406336.
[10]
DELLINGER F, DELON J, GOUSSEAU Y, et al. SAR-SIFT: A SIFT-like algorithm for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53 (1): 453-466. doi: 10.1109/TGRS.2014.2323552.
[11]
HARRIS C and STEPHENS M. A combined corner and edge detector[C]. Proceedings of the Fourth Alvey Vision Conference, Manchester, UK. 1988: 147-151.
[12]
ROSTEN E, PORTER R, and DRUMMOND T. Faster and better: a machine learning approach to corner detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(1): 105-119. doi: 10.1109/ TPAMI.2008.275
[13]
ZHANG Q, SHEN X, XU L, et al. Rolling guidance filter[C]. European Conference on Computer Vision, Zurich, Switzerland, 2014: 815-830.
[14]
MOHANNA F, MOKHTARIAN F. Performance evaluation of corner detection algorithms under affine and similarity transforms[C]. 12th British Machine Vision Conference, Manchester, UK, 2001: 1-10
[15]
BAY H, TUYTELAARS T, VAN G, et al. SURF: speeded-up robust features[J]. International Journal on Computer Vision and Image Understanding, 2008, 110(3): 346-359. doi: 10.1007/11744023_32.
[16]
LEUTENEGGER S, CHLI M, SIEGWART R, et al. BRISK: Binary robust invariant scalable keypoints[C]. IEEE International Conference on Computer Vision, Barcelona, Spain, 2011: 2548-2555.
[17]
CALONDER M, LEPETIT V, STRECHA C, et al. Brief: Binary robust independent elementary features[C]. European Conference on Computer Vision 2010, Heraklion, Greece, 2010: 778-792.
[18]
ANDONI A and INDYK P. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions[J]. Communications of the Association for Computing Machinery, 2008, 51(1): 117-122.