Recent Advances in Deep Learning for SAR Images: Overview of Methods, Challenges, and Future Directions

Eno Peter; Li-Minn Ang; Kah Phooi Seng; Sanjeev Srivastava

doi:10.3390/s26041143

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Recent Advances in Deep Learning for SAR Images: Overview of Methods, Challenges, and Future Directions

Journal article

Open access

Peer reviewed

Recent Advances in Deep Learning for SAR Images: Overview of Methods, Challenges, and Future Directions

Eno Peter, Li-Minn Ang, Kah Phooi Seng and Sanjeev Srivastava

Sensors , Vol.26(4), pp.1-28

2026

DOI: https://doi.org/10.3390/s26041143

PMCID: PMC12943905

PMID: 41755084

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Abstract

remote sensing

synthetic aperture radar

deep learning

machine learning

image processing

The analysis of Synthetic Aperture Radar (SAR) imagery is essential to modern remote sensing, with applications in disaster management, agricultural monitoring, and military surveillance. A significant challenge is that the complex and noisy nature of SAR data severely limits the performance of traditional machine learning (TML) methods, leading to high error rates. In contrast, deep learning (DL) has recently proven highly effective at addressing these limitations. This study provides a comprehensive review of recent DL advances applied to SAR image despeckling, segmentation, classification, and detection. It evaluates widely adopted models, examines the potential of underutilized ones like GANs and GNNs, and compiles available datasets to support researchers. This review concludes by outlining key challenges and proposing future research directions to guide continued progress in SAR image analysis.

Details

Title: Recent Advances in Deep Learning for SAR Images: Overview of Methods, Challenges, and Future Directions
Authors: Eno Peter - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Li-Minn Ang (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Kah Phooi Seng - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Sanjeev Srivastava - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: Sensors , Vol.26(4), pp.1-28
Publisher: MDPI AG
Date published: 2026
DOI: 10.3390/s26041143
ISSN: 1424-8220
PMID: 41755084; PMC12943905
Copyright note: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Data Availability: Data sharing not applicable.
Organisation Unit: School of Science, Technology and Engineering; Engage Research Lab; Sustainability Research Cluster
Language: English
Record Identifier: 991209386002621
Output Type: Journal article

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