Generalized Few-Shot Semantic Segmentation for Remote Sensing Images

Few-shot segmentation (FSS) techniques enhance pixel-level interpretation of unseen classes while reducing reliance on extensive labeled data. However, FSS still faces significant limitations in practical applications: it is restricted to segmenting novel classes and relies on manually constructed support-query pairs during inference. We are the first to introduce the generalized few-shot segmentation (GFSS) task to remote sensing analysis. It enables simultaneous segmentation of base and novel classes without manual prior interventions. The most intuitive construction is to extend a pretrained base classifier with a novel classifier. Nevertheless, since the latter is aggregated from a limited number of supports while the former is trained on abundant data, this disparity inevitably introduces a base class bias, leading to suboptimal segmentation results. This article proposes a background-aware self-mining prototype learning (BSPL) strategy to address the issues above. Specifically, we design a dynamic prototype update mechanism during training to enhance the model's adaptability in few-shot scenarios and thereby mitigate the base class bias. Considering the intraclass variation and complex background elements in remote sensing images, we customize segmentation guidance for each query through background-aware self-mining, achieving more precise segmentation performance. Compared to peer algorithms, extensive experiments demonstrate that BSPL achieves the best overall segmentation performance for both base and novel classes, indicating its significant practicality.