CFRS-DA:End-To-End Domain Adaptation Semantic Segmentation Framework for Cloudy Remote Sensing

Semantic segmentation has emerged as a prominent research direction due to its pixel-level precision and scene parsing capabilities. Satellite remote sensing images often suffer from significant cloud occlusion, posing challenges in label annotation for semantic segmentation. Current methodologies typically adopt a two-stage approach involving separate cloud removal preprocessing followed by segmentation, which lacks end-to-end solutions and compromises efficiency. This paper proposes an end-to-end framework for cloud-occluded satellite image analysis: the Adversarial Entropy Minimization with Curriculum Self-Training Domain Adaptation Framework (AEM-CurST-DA). The framework first introduces an Atrous Convolution Pyramid Filtering Feature Fusion Encoder Network (ACPF <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${ }^{2}$</tex> Net), which suppresses cloud interference through dilated convolutions and multi-level feature fusion, eliminating conventional preprocessing. Subsequently, an entropy adversarial domain discriminator and curriculum self-training strategy are employed to guide crossdomain feature alignment by minimizing inter-domain confidence entropy discrepancies and selecting high-quality pseudo-labels through entropy filtering. Hybrid source-target domain training further mitigates domain shift. Experimental validation achieves over 50 % mIoU on cross-domain Potsdam and Vaihingen datasets, with mAP scores of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 8. 3 \%}$</tex> (Level-1) and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7 4. 3 \%}$</tex> (Level-2) on a custom marine vessel dataset, demonstrating the framework's effectiveness in achieving efficient semantic segmentation under cloud-occluded conditions.