A SAM-adapted weakly-supervised semantic segmentation method constrained by uncertainty and transformation consistency
Yinxia Cao, Xin Huang, Qihao Weng · International Journal of Applied Earth Observation and Geoinformation · 2025
• SAM-adapted weakly-supervised semantic segmentation method guided by point labels. • Uncertainty-based pseudo-label generation for supervising unlabeled pixels. • Transformation consistency constraint under different data disturbances. • Advanced performance by fine-tuning a small number of parameters (0.9 M) Semantic segmentation of remote sensing imagery is a fundamental task to generate pixel-wise category maps. Existing deep learning networks rely heavily on dense pixel-wise labels, incurring high acquisition costs. Given this challenge, this study introduces sparse point labels, a type of cost-effective weak labels, for semantic segmentation. Existing weakly-supervised methods often leverage low-level visual or high-level semantic features from networks to generate supervision information for unlabeled pixels, which can easily lead to the issue of label noises. Furthermore, these methods rarely explore the general-purpose foundation model, segment anything model (SAM), with strong zero-shot generalization capacity in image segmentation. In this paper, we proposed a SAM-adapted weakly-supervised method with three components: 1) an adapted EfficientViT-SAM network (AESAM) for semantic segmentation guided by point labels, 2) an uncertainty-based pseudo-label generation module to select reliable pseudo-labels for supervising unlabeled pixels, and 3) a transformation consistency constraint for enhancing AESAM’s robustness to data perturbations. The proposed method was tested on the ISPRS Vaihingen dataset (collected from airplane), the Zurich Summer dataset (satellite), and the UAVid dataset (drone). Results demonstrated a significant improvement in mean F1 (by 5.89 %–10.56 %) and mean IoU (by 5.95 %–11.13 %) compared to the baseline method. Compared to the closest competitors, there was an increase in mean F1 (by 0.83 %–5.29 %) and mean IoU (by 1.04 %–6.54 %). Furthermore, our approach requires only fine-tuning a small number of parameters (0.9 M) using cheap point labels, making it promising for scenarios with limited labeling budgets. The code is available at https://github.com/lauraset/SAM-UTC-WSSS .