IEEE Geoscience and Remote Sensing Magazine - December 2022 - 32

Sparse Synthetic
Aperture Radar Imaging
From Compressed
Sensing and Machine
Learning
Theories, applications,
and trends
GANG XU , BANGJIE ZHANG,
HANWEN YU , JIANLAI CHEN ,
MENGDAO XING ,
AND WEI HONG
S
ynthetic aperture radar (SAR) image formation can be
treated as a class of ill-posed linear inverse problems,
and the resolution is limited by the data bandwidth for traditional
imaging techniques via matched filter (MF). The
sparse SAR imaging technology using compressed sensing
(CS) has been developed for enhanced performance, such
as superresolution, feature enhancement, etc. More recently,
sparse SAR imaging from machine learning (ML), including
deep learning (DL), has been further studied, showing great
potential in the imaging area. However, there are still gaps
between the two groups of methods for sparse SAR imaging,
and their connections have not been established.
Digital Object Identifier 10.1109/MGRS.2022.3218801
Date of current version: 1 December 2022
32 2473-2397/22©2022IEEE
In this article, we introduce the related sparse imaging
technologies from the concepts of sparsity, low rank, and
deep priors; present the foundations of sparse SAR imaging
by introducing some important issues, inherent limitations,
and current challenges; and systematically review the CS and
ML techniques typically applied to SAR imaging. In particular,
upcoming methods are also included and recommended
as potential future directions. The purpose of this article is to
give inspiration to readers from the viewpoint of sparse imaging
and promote more research in the SAR imaging field.
INTRODUCTION
SAR is an active, modern, and advanced remote sensor with
the capability of providing high-resolution imagery of observations
[1], [2]. The basic principle of SAR is to employ pulse
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE DECEMBER 2022
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IEEE Geoscience and Remote Sensing Magazine - December 2022

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