Two Dimensional Sparse-Regularization-Based InSAR Imaging with Back-Projection Embedding

TL;DR

This paper introduces a novel 2D sparse regularization-based InSAR imaging framework that embeds scene characteristics and back-projection, eliminating the need for post-processing and improving image quality directly from raw data.

Contribution

The paper proposes a new InSAR imaging method combining 2D sparse regularization with back-projection embedding, enhancing quality and reducing post-processing.

Findings

Higher quality interferograms directly from raw data.

Effective under-sampling conditions.

Outperforms conventional methods in image quality.

Abstract

Interferometric Synthetic Aperture Radar (InSAR) Imaging methods are usually based on algorithms of match-filtering type, without considering the scene's characteristic, which causes limited imaging quality. Besides, post-processing steps are inevitable, like image registration, flat-earth phase removing and phase noise filtering. To solve these problems, we propose a new InSAR imaging method. First, to enhance the imaging quality, we propose a new imaging framework base on 2D sparse regularization, where the characteristic of scene is embedded. Second, to avoid the post processing steps, we establish a new forward observation process, where the back-projection imaging method is embedded. Third, a forward and backward iterative solution method is proposed based on proximal gradient descent algorithm. Experiments on simulated and measured data reveal the effectiveness of the proposed method. Compared with the conventional method, higher quality interferogram can be obtained directly from raw echoes without post-processing. Besides, in the under-sampling situation, it's also applicable.