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Analyzing the Adversarial Robustness and Interpretability of Deep SAR Classification Models: A Comprehensive Examination of Their Reliability

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Published in:Remote Sensing vol. 17, no. 11 (2025), p. 1943
Main Author: Chen Tianrui
Other Authors: Zhang Limeng, Guo Weiwei, Zhang Zenghui, Datcu Mihai
Published:
MDPI AG
Subjects:
Environmental monitoring
Accuracy
Classification systems
Datasets
Deep learning
Land use
Network reliability
Artificial neural networks
Synthetic aperture radar
Sensors
Neural networks
Classification
Image classification
Methods
Radar imaging
Land cover
Explainable artificial intelligence
Robustness
Online Access:Citation/Abstract
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Abstract:Deep neural networks (DNNs) have shown strong performance in synthetic aperture radar (SAR) image classification. However, their “black-box” nature limits interpretability and poses challenges for robustness, which is critical for sensitive applications such as disaster assessment, environmental monitoring, and agricultural insurance. This study systematically evaluates the adversarial robustness of five representative DNNs (VGG11/16, ResNet18/101, and A-ConvNet) under a variety of attack and defense settings. Using eXplainable AI (XAI) techniques and attribution-based visualizations, we analyze how adversarial perturbations and adversarial training affect model behavior and decision logic. Our results reveal significant robustness differences across architectures, highlight interpretability limitations, and suggest practical guidelines for building more robust SAR classification systems. We also discuss challenges associated with large-scale, multi-class land use and land cover (LULC) classification under adversarial conditions.
ISSN:2072-4292
DOI:10.3390/rs17111943
Source:Advanced Technologies & Aerospace Database