Adaptive Endmembers Learning-Based Deep Unmixing Network for Hyperspectral Change Detection

Hyperspectral image (HSI) change detection can detect subtle land surface change information, which is of great significance for promoting the sustainable development of human beings. Different from traditional methods, deep learningbased methods can effectively extract more discriminative features, but the problem of mixed pixels is still a challenge due to the low spatial resolution HSI. In this study, an Adaptive Endmembers Learning (AEL)-based deep unmixing network has been proposed for the change detection task, which can perform an unsupervised unmixing through adaptive endmembers learning and then obtain both the binary and multi-class change detection results. Experiments on the China dataset and the USA dataset have shown that AEL performs better than current state-of-the-art methods.