Local Information-Enhanced Graph-Transformer for Hyperspectral Image Change Detection With Limited Training Samples

Abstract

Hyperspectral image (HSI) change detection is a challenging task that focuses on identifying the differences between multi-temporal HSIs. The recent advancement of convolutional neural network (CNN) has made great progress on HSIs change detection. However, due to the limited receptive field, most CNN based change detection models trained with sufficient labeled samples cannot flexibly model the global information that is essential for distinguishing complex objects, thereby achieving relatively-low performance. In this paper, we propose a dual-branch local information enhanced graph-transformer change detection network to fully exploit the local-global spectral-spatial features of the multi-temporal HSIs with limited training samples for change recognition. Specifically, the proposed network is composed of a cascaded of local information enhanced graph-transformer (LIEG) blocks, which jointly extracts local-global features by learning local information representation to enhance the information of graph-transformer. A novel graph-transformer is developed to model global spectral–spatial correlation between graph nodes, enabling the spectral information preservation of HSIs and accurate change detection of areas with various sizes. Extensive experiments have proved that our method achieves significant performance improvement than other state-of-the-art methods on four commonly used HSI datasets.