Cross-Task Meta-Learning Network With Graph-Enhanced Attention Module for Hyperspectral Change Detection

Hyperspectral change detection (HCD) with limited training samples has attracted increasing attention in recent years. The current research works require to train a dedicated model for each dataset, resulting in limited generalization. Moreover, for the same dataset, a model that has been trained for binary change detection (CD) task usually needs to be retrained for multiclass CD task, thereby leading to low efficiency. We propose a cross-task meta-learning network with graph-enhanced attention module (CMGA) for both binary CD tasks and multiclass CD tasks, aiming to fully extract the spatial-spectral features of hyperspectral images (HSIs) and efficiently transfer the prior knowledge to accomplish various HCD tasks with limited training samples. In order to capture general prior knowledge, the multitask meta-optimization is designed to train the pretraining model with the ability of addressing cross-task new classes. Moreover, the diverse tasks are unified into one framework, which aims at identifying the target class from other classes to improve the model efficiency and generalization. The graph-enhanced attention (GA) module is developed to leverage the graph knowledge extract from the graph transformer (GT) module to enhance the pixel-level feature representation. Experiments on four hyperspectral datasets illustrated the effectiveness of the proposed algorithm.