arXiv 2501.107362025-07-01cs.CVcs.AI

基于多尺度不确定性一致性与跨教师-学生注意力的遥感图像半监督语义分割

Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention

Faculty of Data Science, City University of Macau（数据科学学院，澳门城市大学）；WMG, University of Warwick（沃里克大学工程学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航天信息研究所）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子、电气与通信工程学院）；Department of Automation, Tsinghua University（清华大学自动化系）

Shanwen Wang, Xin Sun, Changrui Chen, Danfeng Hong, Jungong Han

DOI：: 10.1109/TGRS.2025.3585489

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AI总结：

针对遥感图像多尺度特征丰富且类间相似度高的问题，提出半监督MUCA模型，通过多尺度不确定性一致性正则化与跨教师-学生注意力机制，结合弱强增强策略，有效提升了无标签数据上的分割性能与相似目标区分能力。

摘要：

半监督学习为遥感（RS）图像分割提供了一种极具吸引力的解决方案，能够减轻劳动密集型的像素级标注负担。然而，RS图像带来了独特的挑战，包括丰富的多尺度特征和较高的类间相似性。为了解决这些问题，本文提出了一种用于RS图像语义分割任务的新型半监督多尺度不确定性与跨教师-学生注意力（MUCA）模型。具体而言，MUCA通过引入多尺度不确定性一致性正则化，约束网络不同层特征图之间的一致性。它提升了半监督算法在无标签数据上的多尺度学习能力。此外，MUCA利用跨教师-学生注意力机制引导学生网络，通过来自教师网络的互补特征，引导学生网络构建更具判别性的特征表示。该设计有效整合了弱增强和强增强（WA和SA），以进一步提升分割性能。为了验证模型的有效性，我们在ISPRS-Potsdam和LoveDA数据集上进行了广泛实验。实验结果表明我们的方法优于最先进的半监督方法。值得注意的是，我们的模型在区分高度相似的目标方面表现出色，展示了其推进半监督RS图像分割任务的潜力。

英文摘要：

Semi-supervised learning offers an appealing solution for remote sensing (RS) image segmentation to relieve the burden of labor-intensive pixel-level labeling. However, RS images pose unique challenges, including rich multi-scale features and high inter-class similarity. To address these problems, this paper proposes a novel semi-supervised Multi-Scale Uncertainty and Cross-Teacher-Student Attention (MUCA) model for RS image semantic segmentation tasks. Specifically, MUCA constrains the consistency among feature maps at different layers of the network by introducing a multi-scale uncertainty consistency regularization. It improves the multi-scale learning capability of semi-supervised algorithms on unlabeled data. Additionally, MUCA utilizes a Cross-Teacher-Student attention mechanism to guide the student network, guiding the student network to construct more discriminative feature representations through complementary features from the teacher network. This design effectively integrates weak and strong augmentations (WA and SA) to further boost segmentation performance. To verify the effectiveness of our model, we conduct extensive experiments on ISPRS-Potsdam and LoveDA datasets. The experimental results show the superiority of our method over state-of-the-art semi-supervised methods. Notably, our model excels in distinguishing highly similar objects, showcasing its potential for advancing semi-supervised RS image segmentation tasks.

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