SAR tomography imaging based on Generalized Orthogonal Matching Pursuit-The case study of pangu 7 star hotel in Beijing

• Published in: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) pp. 6665 - 6668
• Main Authors: Shuguang He, Lei Pang, Xuedong Zhang, Hui Liu, Hui Bi, Liping Ai, Mengxin Sun, Yong Wang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2016
• Subjects: Compressed sensing; compressive sensing; generalized orthogonal matching pursuit; Image resolution; Matching pursuit algorithms; Signal processing algorithms; Synthetic aperture radar; Tomography; tomography imaging
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS.2016.7730740

Compressive sensing theory can recover the original signal from little observation data, by means of the non-adaptive measurement far below Nyquist sampling and the optimization method. Due to the number of track is limited and the uneven distribution of tracks, the traditional method is difficult to image effectively in the practical application, while the compressive sensing theory by tomoSAR three-dimensional imaging (MUSIC, APES, etc.) can solve the problem easily, and it can realize high resolution imaging of SAR in the height direction. This paper applied the Generalized Orthogonal Matching Pursuit (GOMP) algorithm to the SAR tomography imaging, which can accurately reconstruct the signal in the height direction. Meanwhile, in this paper, 14 images of ascend track from which is "TerraSAR-X" HH polarization mode in Beijing during the period of 2011-2014 were taken as the test data, and Beijing pangu7 star hotel is taken as the research target. Finally, the result indicates that our algorithm is better than typical Orthogonal Matching Pursuit (OMP) algorithm.