SAR Coregistration by Robust Selection of Extended Targets and Iterative Outlier Cancellation

IF 4 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters Pub Date : 2022-01-01 DOI:10.1109/lgrs.2021.3132661

L. Pallotta, G. Giunta, C. Clemente, J. Soraghan

引用次数: 5

Abstract

This letter extends the constrained least-squares (CLS) optimization method developed to coregister multitemporal synthetic aperture radar (SAR) images affected by a joint rotation effect and range/azimuth shifts enforcing the absence of zooming effects. To take advantage of the structural information extracted from the scene, the method starts with a detection stage that identifies extended targets/areas in the images. The selected tie-points allow the CLS problem to be reformulated to find its (initial) solution based on a robust subset of image blocks. Then, the mean square error (MSE) of each equation evaluated from the initial solution allows to implement an iterative cancellation procedure to further skim the CLS equation set. The effectiveness of the proposed procedure is validated on real SAR data in comparison with the standard CLS.

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基于扩展目标鲁棒选择和迭代离群值抵消的SAR共配准

这封信扩展了约束最小二乘(CLS)优化方法，该方法开发用于受联合旋转效应和距离/方位角位移影响的多时相合成孔径雷达(SAR)图像的共配准，从而强制缺少缩放效应。为了利用从场景中提取的结构信息，该方法从识别图像中扩展目标/区域的检测阶段开始。所选的结合点允许重新定义CLS问题，以便根据图像块的健壮子集找到其(初始)解决方案。然后，从初始解评估的每个方程的均方误差(MSE)允许实现迭代消去程序，以进一步撇去CLS方程集。通过与标准CLS的比较，在实际SAR数据上验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Geoscience and Remote Sensing Letters 工程技术-地球化学与地球物理

CiteScore

7.60

自引率

12.50%

发文量

1113

审稿时长

3.4 months

期刊介绍： IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.