A modified sparse reconstruction method for three-dimensional synthetic aperture radar image

Ziqiang Zhang; Kefeng Ji; Haibo Song; Huanxin Zou

doi:10.1117/12.2289440

8 March 2018 A modified sparse reconstruction method for three-dimensional synthetic aperture radar image

Ziqiang Zhang, Kefeng Ji, Haibo Song, Huanxin Zou

Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106110R (2018) https://doi.org/10.1117/12.2289440
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l₀ norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l₀ norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

Citation Download Citation

Ziqiang Zhang, Kefeng Ji, Haibo Song, and Huanxin Zou "A modified sparse reconstruction method for three-dimensional synthetic aperture radar image", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106110R (8 March 2018); https://doi.org/10.1117/12.2289440

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KEYWORDS

Reconstruction algorithms

3D image processing

Synthetic aperture radar

3D image reconstruction

Data modeling

3D acquisition

3D modeling

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