Adaptive Self-Supporting Prototype Learning for Remote Sensing Few-Shot Semantic Segmentation

The semantic segmentation of remote sensing images with few shots has important theoretical and application value. Most of the existing few-shot semantic segmentation frameworks are based on prototype learning methods, in which a single support prototype is designed to guide the query set for prediction. However, the visual differences between the support set and the query set make it difficult for a single support prototype, generated from the support set, to comprehensively encapsulate the semantic information of all the query images. This article introduces an adaptive self-supporting prototype learning network designed for few-shot segmentation (FSS), in order to tackle the challenges mentioned earlier. We propose adaptive hyperprototype representation (HPR), which consists of hyperprototype clustering (HPC) and guided prototype matching (GPM), to generate and assign multiple representative prototypes to compensate for the limitations of a single prototype in representing the semantic information of the query images. Specifically, HPC is a parameter-free and adaptive approach, which can extract more representative prototypes by aggregating similar feature vectors utilizing superpixel feature clustering. Meanwhile, GPM can select matched prototypes to provide more accurate guidance, allowing for uniformly aligned representation of multiple prototypes and complex image semantic information. We also introduce self-supporting matching (SSM) prototype learning, which can accurately guide the query set segmentation by acquiring query set prototypes. SSM generates initial pseudo labels for the query set based on the support set prototypes, and further guides the query set using the pseudo labels, along with the query prototypes generated by its own features, thus effectively avoiding visual differences between the support set and query set. The proposed adaptive self-supporting prototype learning network substantially improves the prototype quality and achieves a superior performance on object-level remote sensing datasets.