Sparse SAR Imaging and Quantitative Evaluation Based on Nonconvex and TV Regularization

Zhongqiu Xu,Yirong Wu,Bingchen Zhang,Guoru Zhou,Lihua Zhong

doi:10.3390/rs13091643

Abstract

Sparse signal processing has been used in synthetic aperture radar (SAR) imaging due to the maturity of compressed sensing theory. As a typical sparse reconstruction method, L1 regularization generally causes bias effects as well as ignoring region-based features. Our team has proposed to linearly combine the nonconvex penalty and the total variation (TV)-norm penalty as a compound regularizer in the imaging model, called nonconvex and TV regularization, which can not only reduce the bias caused by L1 regularization but also enhance point-based and region-based features. In this paper, we use the variable splitting scheme and modify the alternating direction method of multipliers (ADMM), generating a novel algorithm to solve the above optimization problem. Moreover, we analyze the radiometric properties of sparse-signal-processing-based SAR imaging results and introduce three indexes suitable for sparse SAR imaging for quantitative evaluation. In experiments, we process the Gaofen-3 (GF-3) data utilizing the proposed method, and quantitatively evaluate the reconstructed SAR image quality. Experimental results and image quality analysis verify the effectiveness of the proposed method in improving the reconstruction accuracy and the radiometric resolution without sacrificing the spatial resolution.

Highlights

synthetic aperture radar (SAR) is the major modern microwave imaging technology in remote sensing and is capable of producing high-resolution images of the Earth’s surface
To quantitatively evaluate the reconstructed SAR image quality based on nonconvex and total variation (TV) regularization, this paper analyzes the radiometric properties of sparse-signal-processing-based SAR
Experimental results and image quality analysis verify the effectiveness and advantages of the method: compared with L1 regularization, the method can improve the reconstruction accuracy and enhance region-based features represented by the radiometric resolution; compared with the matched filtering method, the method can suppress speckles as well as sidelobes and additive noise; compared with the method of speckle removal in the image domain including multilook processing [24,25], the method can process the raw echo data and generate SAR images after speckle reduction, without sacrificing the spatial resolution

Summary

Introduction

SAR is the major modern microwave imaging technology in remote sensing and is capable of producing high-resolution images of the Earth’s surface. With the development of compressed sensing, sparse signal processing has been widely used in SAR imaging, forming a novel strategy of SAR imaging named sparse SAR imaging. The sparse SAR imaging system can reconstruct a sparse signal with far fewer samples than that required by Nyquist–Shannon theory; in addition, it can improve the reconstruction performance under full sampling conditions [1,2,3]. Researchers have applied sparse SAR imaging to a variety of SAR imaging modes, including stripmap SAR, ScanSAR, spotlight SAR and TOPS SAR modes [2,4,5].

Methods

Results

Discussion

Conclusion