Frequency Decomposition-Driven Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation

• URL: http://arxiv.org/abs/2404.04531v1
• Date: Sat, 6 Apr 2024 07:13:49 GMT
• Title: Frequency Decomposition-Driven Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation
• Authors: Xianping Ma, Xiaokang Zhang, Xingchen Ding, Man-On Pun, Siwei Ma,
• Abstract summary: Cross-domain semantic segmentation of remote sensing (RS) imagery based on unsupervised domain adaptation (UDA) techniques has significantly advanced deep-learning applications in the geosciences. It is still challenging to retain cross-domain local spatial details and global contextual semantics simultaneously. We propose novel high/low-frequency decomposition (HLFD) techniques to guide representation alignment in cross-domain semantic segmentation.
• Score: 30.606689882397223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain semantic segmentation of remote sensing (RS) imagery based on unsupervised domain adaptation (UDA) techniques has significantly advanced deep-learning applications in the geosciences. Recently, with its ingenious and versatile architecture, the Transformer model has been successfully applied in RS-UDA tasks. However, existing UDA methods mainly focus on domain alignment in the high-level feature space. It is still challenging to retain cross-domain local spatial details and global contextual semantics simultaneously, which is crucial for the RS image semantic segmentation task. To address these problems, we propose novel high/low-frequency decomposition (HLFD) techniques to guide representation alignment in cross-domain semantic segmentation. Specifically, HLFD attempts to decompose the feature maps into high- and low-frequency components before performing the domain alignment in the corresponding subspaces. Secondly, to further facilitate the alignment of decomposed features, we propose a fully global-local generative adversarial network, namely GLGAN, to learn domain-invariant detailed and semantic features across domains by leveraging global-local transformer blocks (GLTBs). By integrating HLFD techniques and the GLGAN, a novel UDA framework called FD-GLGAN is developed to improve the cross-domain transferability and generalization capability of semantic segmentation models. Extensive experiments on two fine-resolution benchmark datasets, namely ISPRS Potsdam and ISPRS Vaihingen, highlight the effectiveness and superiority of the proposed approach as compared to the state-of-the-art UDA methods. The source code for this work will be accessible at https://github.com/sstary/SSRS.

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