Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation using Frequency Decomposition and Global-Local Context Modeling

The core message of this work is to propose a novel unsupervised domain adaptation (UDA) framework called FD-GLGAN that leverages frequency decomposition techniques and global-local context modeling to improve the cross-domain transferability and generalization capability of semantic segmentation models for remote sensing images.