Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation
This work addresses domain adaptation challenges in geosciences for more effective use of remote sensing imagery, though it appears incremental as it builds on existing UDA methods.
The paper tackles the problem of unsupervised domain adaptation for remote sensing image semantic segmentation by proposing a decomposition scheme and a global-local generative adversarial network to improve cross-domain transferability, achieving state-of-the-art results on benchmarks like ISPRS Potsdam and Vaihingen.
Unsupervised domain adaptation (UDA) techniques are vital for semantic segmentation in geosciences, effectively utilizing remote sensing imagery across diverse domains. However, most existing UDA methods, which focus on domain alignment at the high-level feature space, struggle to simultaneously retain local spatial details and global contextual semantics. To overcome these challenges, a novel decomposition scheme is proposed to guide domain-invariant representation learning. Specifically, multiscale high/low-frequency decomposition (HLFD) modules are proposed to decompose feature maps into high- and low-frequency components across different subspaces. This decomposition is integrated into a fully global-local generative adversarial network (GLGAN) that incorporates global-local transformer blocks (GLTBs) to enhance the alignment of decomposed features. By integrating the HLFD scheme and the GLGAN, a novel decomposition-based UDA framework called De-GLGAN is developed to improve the cross-domain transferability and generalization capability of semantic segmentation models. Extensive experiments on two UDA benchmarks, namely ISPRS Potsdam and Vaihingen, and LoveDA Rural and Urban, demonstrate the effectiveness and superiority of the proposed approach over existing state-of-the-art UDA methods. The source code for this work is accessible at https://github.com/sstary/SSRS.