A Joint Saliency Temporal–Spatial–Spectral Information Network for Hyperspectral Image Change Detection

Abstract

Hyperspectral image change detection (HSI-CD) is a fundamental task in the field of remote sensing (RS) observation, which utilizes the rich spectral and spatial information in bitemporal HSIs to detect subtle changes on the Earth's surface. However, modern deep learning (DL)-based HSI-CD methods mostly rely on patch-based methods, which leads to spectral band redundancy and spatial information noise in limited receiving domains, thus ignoring the extraction and utilization of saliency information and limiting the improvement of CD performance. To address these issues, this article proposes a joint saliency temporal–spatial–spectral information network (STSS-Net) for HSI-CD. The principal contributions of this article can be summarized: 1) we have designed a spatial saliency information extraction (SSIE) module for denoising based on distance from center pixels and spectral similarity of the substance, which increases the attention to spatial differences between similar spectral substances and different spectral substances; 2) we have designed a compact high-level spectral information tokenizer (CHLSIT) for spectral saliency information, where the high-level conceptual information of changes in spectral interest can be represented by nonlinear combinations of spectral bands, and redundancy can be removed by extracting high-level spectral conceptual features; and 3) utilizing the advantages of CNN and transformer architectures to combine temporal–spatial–spectral information. The experimental results on three real HSI-CD datasets show that STSS-Net can improve the accuracy of CD and has a certain improvement in the detection of edge information and complex information.