收稿日期: 2015-01-20
修回日期: 2015-04-28
网络出版日期: 2016-01-15
Deformation detection approach for repeat-passInSAR based on distributed compressed sensing
Received date: 2015-01-20
Revised date: 2015-04-28
Online published: 2016-01-15
田野 , 张冰尘 , 洪文 . 基于分布式压缩感知的重轨干涉SAR形变检测方法与实验[J]. 中国科学院大学学报, 2016 , 33(1) : 107 -114 . DOI: 10.7523/j.issn.2095-6134.2016.01.016
A model for repeat-pass interferometric synthetic aperture radar (InSAR) is proposed to detect deformation based on distributed compressed sensing (DCS). DCS is applied to recover the signals from independent observations of multiple sensors, which are sparse in a transform domain and coherent with each other. We conduct a series of ground-based SAR experiments to compare the recovery performance using different imaging algorithms, such as compressed sensing (CS) and DCS. We find that images recovered by CS and DCS can preserve the phase information of the complex data and have better focus and lower sidelobes than those by Omega-k. Furthermore, by taking advantage of the joint sparsity among multiple scene echoes, DCS joint observation system can further reduce independent observations than CS and achieve scene reconstruction and deformation detection.
[1] Rosen P A, Hensley S, Joughin I R, et al. Synthetic aperture radar interferometry[J]. Proceedings of the IEEE, 2000, 88(3): 333-382.
[2] Charles V J, Daniel E W, Paul H E, et al. Spotlight-mode synthetic aperture radar: a signal processing approach[M]. Kiluwer Academic Publishers, Boston, USA, 1996.
[3] Zhang B C, Hong W, Wu Y R. Sparse microwave imaging: principles and applications[J]. Science China Information Sciences, 2012, 55(8): 1 722-1 754.
[4] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1 289-1 306.
[5] Donoho D L, Tanner J. Precise undersampling theorems[J]. Proceeding of the IEEE, 2010, 98: 913-924.
[6] Patel V M, Easley G R, Healy D M, et al. Compressed synthetic aperture radar[J]. IEEE Transactions on Signal Processing, 2010, 4(2): 244-254.
[7] Zhang B C, Jiang H, Hong W, et al. Synthetic aperture radar imaging of sparse targets via compressed sensing [C]//Synthetic Aperture Radar (EUSAR), 2010 8th European Conference on. Aachen Germany, 2010: 1- 4.
[8] Duarte M F, Sarvotham S, Baron D, et al. Distributed compressed sensing of jointly sparse signals [C]//Asilomar Conf Signals, Sys, Comput. 2005: 1 537-1 541.
[9] Baron D, Duarte M F, Wakin M B, et al. Distributed compressed sensing [R/OL].[201501-10]. TREE0612, Rice University, Houston, TX, Nov. 2006. http://dsp.rice.edu/cs/.
[10] Lin Y G, Zhang B C, Hong W, et al. Multi-channel SAR imaging based on distributed compressive sensing[J]. Science China Information Sciences, 2012, 55(2): 245-259.
[11] Lin Y G, Zhang B C, Hong W, et al. Along-track interferometric sar imaging based on distributed compressed sensing[J]. IEEE Electronics Letters, 2010, 46(12): 858-860.
[12] Prunte L. Application of distributed compressed sensing for GMTI purposes [C]//IET International Conference on Radar Systems. 2012: 22-25.
[13] Aguilera E, Nannini M, Reigber A. Multi-signal compressed sensing for polarimetric SAR tomography [C]// IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2011: 1 369-1 372.
[14] Wang Y P, Tan W X, Hong W, et al. Ground-based SAR for man-made structure Deformation monitoring [C]//1st International Workshop Spatial Information Technologies for Monitoring the Deformation of Large-Scale Man-made Linear Features. Hong Kong, 2010: 55-65.
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