Cross-Domain Few-Shot Segmentation for Remote Sensing Image Based on Task Augmentation and Feature Disentanglement

Few-shot segmentation aims to segment a large number of unlabeled samples in the target domain, by leveraging the images and labels from the source domain as well as a few labeled samples from the target domain. This is pivotal in tackling the scarcity of labeled samples in remote sensing image segmentation tasks. However, prevalent few-shot segmentation methods overlook inter-domain discrepancies, do not model and leverage the relationship between samples, and often only implement binary classification but not multi-class classification directly. To address these problems, we propose a cross-domain few-shot segmentation method based on task augmentation and feature disentanglement for practical remote sensing segmentation tasks. On one hand, task augmentation, which involves increasing the diversity of the training set and generating more challenging training data, can improve the model's generalization. On the other hand, feature disentanglement, involving the extraction of domain-irrelevant features for segmentation, improves the transferability of the model. Furthermore, to flexibly capture the relationships between the segmented regions, a graph with regions as nodes and relationships between nodes as edges is constructed. Then, labels are propagated from the labeled nodes to the unlabeled nodes in the graph by label propagation algorithm to implement multi-class classification directly. We conducted experiments on two public datasets as well as a Tibetan Plateaudataset collected by our group.And the experimental results show that the proposed method leads to a significant improvement in accuracy compared to existing methods, demonstrating its effectiveness.