Reconstruction-Assisted and Distance-Optimized Adversarial Training: A Defense Framework for Remote Sensing Scene Classification

Despite deep neural networks (DNNs) have been widely applied in remote sensing (RS) scene classification and achieved satisfying performance, the vulnerability of DNNs toward adversarial examples significantly degrades their performance. Moreover, the relatively limited labeled samples of RS scene classification make DNNs more likely to overfit, leading to weak generalizability and noise sensitivity. This may result in DNNs being more vulnerable to adversarial examples. Consequently, the defense of adversarial examples is of crucial importance to improve both the generalizability and robustness of DNNs in the RS scene classification task. However, few studies have been conducted on defense for RS scene classification, especially ignoring the intrinsic characteristics of RS images. In this article, an effective defense framework for RS scene classification, named reconstruction-assisted and distance-optimized adversarial training (RDAT), is proposed to defend adversarial examples. To solve the problems caused by high interclass similarity, a distance-optimized (DO) strategy is designed for adversarial training (AT) to strengthen the learning of underfitting content, increase the interclass distance, and improve the robustness of the networks. Furthermore, to generate high-quality samples for AT, a reconstruction-assisted (RA) block is proposed to eliminate adversarial perturbations in adversarial examples. Specifically, in this block, by Swin Transformer (SwinT) block and multiscale convolution (MSC) block, SwinT-MSC-UNet (SMUNet) is constructed to fully extract global and multiscale local features to adapt to the characteristics of RS images with a large variance of ground object scales. Extensive experiments on the benchmark datasets, that is, UC Merced (UCM) and aerial image dataset (AID), have demonstrated that the proposed RDAT can effectively resist multiple adversarial attacks and yield superior results than other defense methods for RS scene classification.