Pyramid self-attention mechanism-based change detection in hyperspectral imagery

Abstract

To address the problem of “pseudochange” caused by illumination, phasing, and shadows in multiperiod remote sensing images, a bitemporal, hyperspectral remote sensing image change detection method is proposed. The method uses a pyramid self-attention mechanism, and the attention module is introduced to simulate the attention mechanism of human eyes, where more attention is given to a small number of important objects. The model architecture uses a general encoder–decoder paradigm, in which shared encoders extract common features, whereas individual decoders learn task-specific representations. The classifier takes fused data to locate where the changes occurred. The method is tested on the ZY1-CD dataset.