Image and Signal Processing Methods

Lq regularization-based unobserved baselines’ data estimation method for tomographic synthetic aperture radar inversion

[+] Author Affiliations
Hui Bi

University of Chinese Academy of Sciences, No. 19, Yu Quan Lu, Beijing 100049, China

Chinese Academy of Sciences, Institute of Electronics, Science and Technology on Microwave Imaging Laboratory, No. 19, North 4th Ring Road West, Haidian District, Beijing 100190, China

Bingchen Zhang, Wen Hong

Chinese Academy of Sciences, Institute of Electronics, Science and Technology on Microwave Imaging Laboratory, No. 19, North 4th Ring Road West, Haidian District, Beijing 100190, China

J. Appl. Remote Sens. 10(3), 035014 (Aug 12, 2016). doi:10.1117/1.JRS.10.035014
History: Received April 18, 2016; Accepted July 28, 2016
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Abstract.  The elevation image quality of tomographic synthetic aperture radar (TomoSAR) data depends mainly on the elevation aperture size, number of baselines, and baseline distribution. In TomoSAR, due to the restricted number of baselines with irregular distributions, the elevation imaging quality is always unacceptable using the conventional spectral analysis approach. Therefore, for a given limited number of irregular baselines, the completion of data for the unobserved virtual uniform baseline distribution should be addressed to improve the spectral analysis-based TomoSAR reconstruction quality. We propose an Lq(0<q1) regularization-based unobserved baselines’ data estimation method for TomoSAR, which uses the geometric imaging relationship between the observed and unobserved baseline distributions. In the proposed method, we first estimate the transformation matrix between the acquisitions and the data of virtual uniform baseline distribution by solving an optimization problem, before calculating the data for virtual baseline distribution based on the acquisitions and the transformation matrix. Finally, the elevation reflectivity function is recovered using the spectral analysis method based on the estimated data. Compared with the reconstructed results only based on the limited irregular acquisitions, the image recovered using the dataset with a virtual uniform baseline distribution can improve the elevation image quality in an efficient manner.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Hui Bi ; Bingchen Zhang and Wen Hong
"Lq regularization-based unobserved baselines’ data estimation method for tomographic synthetic aperture radar inversion", J. Appl. Remote Sens. 10(3), 035014 (Aug 12, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.035014


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