Reducing the reliance of remote sensing semantic segmentation models on labeled training data is essential for practical model deployment. Self-supervised pre-training methods, which learn representations from unlabeled data by designing pretext tasks, provide an approach to address this requirement. One inconvenience of the currently contrastive learning-based and masked image modeling-based self-supervised methods is the difficulty in evaluating the quality of the pre-trained model without fine-tuning for semantic segmentation task. Hence, this paper proposes a pixel-level clustering-based self-supervised learning method, named SDCluster, which allows for a qualitative evaluation of the pre-trained model through visualizing the clustering results. Specifically, SDCluster extends the self-distillation framework to the pixel-level by incorporating the clustering assignment module. Then, clustering constraint modules, including prototype constraint module and semantic consistency constraint module, are designed to eliminate ineffective cluster prototypes and preserve the semantic information of ground objects. Benefiting from the correlation between pixel-level clustering and per-pixel classification of semantic segmentation, experimental results indicate that SDCluster exhibits competitive fine-tuning accuracy and robust few-shot segmentation capabilities when compared to prevalent self-supervised methods. Large-scale pre-training experiment and practical application experiment also prove the generalization ability and extensibility of the proposed method. The code and the dataset for practical application experiment are available at https://github.com/openrsgis/SDCluster.

中文翻译：

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SDCluster： 一种基于聚类的自监督预训练方法，用于遥感图像的语义分割


减少遥感语义分割模型对标记训练数据的依赖对于实际模型部署至关重要。自我监督的预训练方法，通过设计前置任务从未标记的数据中学习表示，提供了一种满足这一要求的方法。目前基于对比学习和基于掩码图像建模的自我监督方法的一个不便之处在于，如果不对语义分割任务进行微调，就难以评估预训练模型的质量。因此，本文提出了一种基于像素级聚类的自我监督学习方法，名为 SDCluster，该方法允许通过可视化聚类结果对预训练模型进行定性评估。具体来说，SDCluster 通过整合聚类分配模块，将自蒸馏框架扩展到像素级。然后，设计聚类约束模块，包括原型约束模块和语义一致性约束模块，以消除无效的聚类原型并保留地面目标的语义信息;得益于像素级聚类和语义分割的每像素分类之间的相关性，实验结果表明，与流行的自监督方法相比，SDCluster 表现出有竞争力的微调精度和强大的小样本分割能力。大规模的预训练实验和实际应用实验也证明了所提方法的泛化能力和可扩展性。用于实际应用实验的代码和数据集可在 https://github.com/openrsgis/SDCluster 获取。