Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization

Yangyang Wang,Xu Zhan,Liming Zhou,Zhiming He,Yuanhua Fu

doi:10.3390/rs14020288

Abstract

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–minimization (MM) and L1 regularization (MM-L1) to improve SAR image quality. Next, we combine MM and L1/2 regularization (MM-L1/2) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and L0 regularization (MM-L0) to obtain 3D images. Finally, we present a generalized MM-Lq algorithm (GMM-Lq) for sparse SAR imaging problems with arbitrary q0≤q≤1 values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.

Highlights

Synthetic aperture radar (SAR) is an active all-day, all-weather microwave imaging technology that is widely used in remote sensing [1,2], geographic disaster detection [3], security inspection [4], and aircraft stealth performance testing [5]
Compared with conventional sparse reconstruction methods based on the observation matrix, the algorithm in this paper reduces the calculation time and retains the phase information (PI) of the image, which allows the reconstructed image to be applied to the fields requiring phase information
We used target-to-background ratio (TBR) and image entropy (ENT) [33] as the quantitative evaluation criteria to evaluate the effect of the sparse reconstruction algorithm

Summary

Introduction

Synthetic aperture radar (SAR) is an active all-day, all-weather microwave imaging technology that is widely used in remote sensing [1,2], geographic disaster detection [3], security inspection [4], and aircraft stealth performance testing [5]. For conventional two-dimensional (2D) SAR imaging, the real three-dimensional (3D) imaging scene is projected onto the 2D range–azimuth plane, prone to shadow effects and height direction aliasing. These defects seriously affect subsequent image interpretation and application. Compressive sensing (CS) has been applied to many fields, such as medical imaging [10,11,12] and geographic remote sensing [13,14,15]

Results

Discussion

Conclusion