Deep neural networks (DNNs) have found widespread applications in interpreting remote sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are vulnerable to different types of noises, particularly adversarial noises. Surprisingly, there has been a lack of comprehensive studies on the robustness of RS tasks, prompting us to undertake a thorough survey and benchmark on the robustness of image classification and object detection in RS. To our best knowledge, this study represents the first comprehensive examination of both natural robustness and adversarial robustness in RS tasks. Specifically, we have curated and made publicly available datasets that contain natural and adversarial noises. These datasets serve as valuable resources for evaluating the robustness of DNNs-based models. To provide a comprehensive assessment of model robustness, we conducted meticulous experiments with numerous different classifiers and detectors, encompassing a wide range of mainstream methods. Through rigorous evaluation, we have uncovered insightful and intriguing findings, which shed light on the relationship between adversarial noise crafting and model training, yielding a deeper understanding of the susceptibility and limitations of various models, and providing guidance for the development of more resilient and robust models

翻译：深度神经网络（DNNs）在遥感图像解译中得到了广泛应用。然而，先前研究表明DNNs容易受到不同类型噪声的影响，特别是对抗性噪声。令人惊讶的是，目前尚缺乏针对遥感任务鲁棒性的全面研究，这促使我们对遥感图像分类与目标检测的鲁棒性展开系统性的调查与基准测试。据我们所知，本研究首次对遥感任务中的自然鲁棒性和对抗鲁棒性进行了全面考察。具体而言，我们整理并公开了包含自然噪声和对抗噪声的数据集，这些数据集可作为评估基于DNNs模型鲁棒性的重要资源。为了全面评估模型鲁棒性，我们针对大量不同分类器和检测器开展了精细实验，覆盖了广泛的主流方法。通过严格评估，我们发现了具有洞察力和启发性的结论，揭示了对抗噪声构造与模型训练之间的关系，加深了对各类模型脆弱性与局限性的理解，并为开发更具韧性和鲁棒性的模型提供了指导。