Topological Information Aggregation Network for Few-Shot Cross-Domain Hyperspectral Image Classification

In recent advancements, hyperspectral image (HSI) classification through few-shot learning (FSL) has significantly progressed. Domain adaptation, integrated with FSL, effectively utilizes transferable knowledge from a source domain (SD) with abundant labeled data to excel in classification tasks within a target domain (TD) with scarce labels. However, most existing methods usually use traditional convolutional neural networks (CNNs) to extract local spatial information to characterize and mine feature and distribution information while ignoring the underlying topological relationships among feature classes. Therefore, we propose a topology graph perception cross-domain FSL (TGP-CFSL) framework that leverages graph information aggregation. Specifically, to construct the extended topological relationships of the target, we have designed a topological graph-based multiscale fusion (TGMF) feature extraction module, which is adept at fully mining the topological spatial neighborhood information of the target. Meanwhile, a dual-graph information perception (DGIP) module is designed, which is able to characterize and aggregate intradomain topological relationships in terms of both feature representations and interdomain distribution similarities and to extract higher order domain distribution information for realizing domain alignment. Experimental results on three public HSI datasets demonstrate that the proposed method outperforms existing methods.