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Deep neural networks (DNNs) have found widespread applications ininterpreting remote sensing (RS) imagery. However, it has been demonstrated inprevious works that DNNs are vulnerable to different types of noises,particularly adversarial noises. Surprisingly, there has been a lack ofcomprehensive studies on the robustness of RS tasks, prompting us to undertakea thorough survey and benchmark on the robustness of image classification andobject detection in RS. To our best knowledge, this study represents the firstcomprehensive examination of both natural robustness and adversarial robustnessin RS tasks. Specifically, we have curated and made publicly available datasetsthat contain natural and adversarial noises. These datasets serve as valuableresources for evaluating the robustness of DNNs-based models. To provide acomprehensive assessment of model robustness, we conducted meticulousexperiments with numerous different classifiers and detectors, encompassing awide range of mainstream methods. Through rigorous evaluation, we haveuncovered insightful and intriguing findings, which shed light on therelationship between adversarial noise crafting and model training, yielding adeeper understanding of the susceptibility and limitations of various models,and providing guidance for the development of more resilient and robust models

A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking