Towards Robust Semantic Segmentation for Remote Sensing Images Under Adversarial Noise Condition

Remote sensing image analysis tasks perform exceptionally well on clear, noise-free remote sensing images. However, their accuracy significantly declines when faced with adversarial conditions, such as noise and interference. To address this challenge, we propose the MDWT-Transformer, which is a transformer-based autoencoder that is enhanced with multi-level discrete wavelet transformations and nonlinear activation free transformer blocks. Our approach uses multi-level discrete wavelet transformations to gradually decrease the size of the image. Then, the image is gradually restored through its inverse transformation. This structure is beneficial for capturing multi-scale information in images. Additionally, the nonlinear activation free transformer block offers a dual advantage by combining lower computational costs with preserving a significant portion of details in the image. Experimental results on the ISPRS Vaihingen and Potsdam datasets demonstrate the MDWT-Transformer’s superiority in enhancing image quality and mitigating adversarial interference compared to advanced autoencoder benchmarks.