SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote Sensing

• URL: http://arxiv.org/abs/2410.13471v2
• Date: Sat, 26 Oct 2024 08:11:12 GMT
• Title: SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote Sensing
• Authors: Bin Wang, Fei Deng, Shuang Wang, Wen Luo, Zhixuan Zhang, Gulan Zhang, Peifan Jiang,
• Abstract summary: UDA enables models to learn from unlabeled target domain data while training on labeled source domain data. We propose integrating contrastive learning into UDA, enhancing the model's capacity to capture semantic information. Our SimSeg method outperforms existing approaches, achieving state-of-the-art results.
• Score: 14.007392647145448
• License:
• Abstract: Semantic segmentation of remote sensing (RS) images is a challenging yet crucial task. While deep learning, particularly supervised learning with large-scale labeled datasets, has significantly advanced this field, acquiring high-quality labeled data is expensive and time-consuming. Additionally, variations in ground sampling distance, imaging equipment, and geographic differences cause domain shifts between datasets, which limit model performance across domains. Unsupervised domain adaptation (UDA) offers a solution by enabling models to learn from unlabeled target domain data while training on labeled source domain data. Recent self-supervised learning approaches using pseudo-label generation have shown promise in addressing domain discrepancies. Combining source and target images with their true and pseudo labels has proven effective in reducing domain bias. However, the use of pseudo-labeling for RS image segmentation is underexplored. Existing methods often rely on high-confidence pixel points as pseudo-labels, reducing supervision in low-confidence areas. Noise in pseudo-labels further weakens the model's ability to learn target domain semantics. While some methods assign confidence weights, noisy pseudo-labels remain an issue. To address these limitations, we propose integrating contrastive learning into UDA, enhancing the model's capacity to capture semantic information by maximizing the similarity between augmented views of the same image. This provides additional supervision to improve performance in the target domain. Extensive experiments on key RS datasets (Potsdam, Vaihingen, LoveDA) demonstrate that our SimSeg method outperforms existing approaches, achieving state-of-the-art results. Visualization and quantitative analyses confirm its superior ability to learn from the target domain. The code is available at \url{https://github.com/woldier/SiamSeg}.

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