利用SAR-FAST角点检测的合成孔径雷达图像配准方法

刘妍; 余淮; 杨文; 李立

doi:10.11999/JEIT160386

利用SAR-FAST角点检测的合成孔径雷达图像配准方法

doi: 10.11999/JEIT160386

刘妍¹,
余淮¹,
杨文^1, ,,
李立¹

基金项目:

国家自然科学基金(61271401, 61331016)

计量
- 文章访问数: 1810
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- 被引次数: 0
出版历程
- 收稿日期: 2016-04-20
- 修回日期: 2016-08-30
- 刊出日期: 2017-02-19

SAR Image Registration Using SAR-FAST Corner Detection

LIU Yan¹,
YU Huai¹,
YANG Wen^{1
, ,},
LI Li¹

Funds:

The National Natural Science Foundation of China (61271401,61331016)

摘要

摘要: 合成雷达孔径图像配准作为变化检测和图像信息融合的基础，对多时相SAR图像的解译具有重要作用。该文提出一种基于SAR-FAST角点检测的图像配准方法。首先，选用迭代引导平滑算法抑制斑点噪声对角点检测的影响；然后，以检测点为圆心，选择合适的检测半径，在圆周上选取检测窗口，统计与检测点不相似的窗口数量，判断检测点是否为角点；最后，对候选角点进行分析，根据其强度分布特点进一步剔除误检点。实验结果表明，SAR-FAST可以检测到足够数量且稳定性和重复性好的角点，应用于图像配准，也能获得较好的配准效果。
- 合成孔径雷达 /
- 角点检测 /
- 特征描述 /
- 图像配准
Abstract: 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.
- SAR /
- Corner detection /
- Feature description /
- Image registration

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参考文献(21)

王国力, 周伟, 柴勇, 等. 基于单演信号理论的SAR图像配准[J]. 电子与信息学报, 2013, 35(8): 1779-1785 doi: 10.3724/ SP.J.1146.2012.01487.

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.

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.

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.

李英杰, 张俊举, 常本康, 等. 一种多波段红外图像联合配准和融合方法[J]. 电子与信息学报, 2016, 38(1): 8-14. doi: 10.11999/JEIT150479.

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.

邓梁, 史仪凯, 张均田. 基于时变医学先验信息的约束成像及图像配准方法[J]. 电子与信息学报, 2013, 35(12): 2942-2947. doi: 10.3724/SP.J.1146.2012.01565.

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.

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.

LOWE D. Object recognition from local scale-invariant features[C]. IEEE International Conference on Computer Vision, Corfu, Greece, 1999: 1150-1157.

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.

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.

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.

HARRIS C and STEPHENS M. A combined corner and edge detector[C]. Proceedings of the Fourth Alvey Vision Conference, Manchester, UK. 1988: 147-151.

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

ZHANG Q, SHEN X, XU L, et al. Rolling guidance filter[C]. European Conference on Computer Vision, Zurich, Switzerland, 2014: 815-830.

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

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.

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.

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.

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.

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