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.

翻译：半监督学习为遥感图像分割提供了一种有吸引力的解决方案，以减轻劳动密集型像素级标注的负担。然而，遥感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。为解决这些问题，本文提出了一种新颖的半监督多尺度不确定性与跨师生注意力模型，用于遥感图像语义分割任务。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未标记数据上的多尺度学习能力。此外，MUCA利用跨师生注意力机制指导学生网络，引导学生网络通过来自教师网络的互补特征构建更具判别性的特征表示。该设计有效整合了弱增强与强增强，以进一步提升分割性能。为验证模型的有效性，我们在ISPRS-Potsdam和LoveDA数据集上进行了大量实验。实验结果表明，我们的方法优于当前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物体方面表现出色，展现了其在推进半监督遥感图像分割任务方面的潜力。