Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention
Journal:
arXiv
Published Date:
Jan 18, 2025
Abstract
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.
