Adaptive Multitask Autoencoder-Based Hyperspectral Unmixing Exploiting Auxiliary Data via Graph Associations

Abstract

Hyperspectral unmixing is a technique in hyperspectral image processing that decomposes the spectra of mixed pixels into pure spectral components (endmembers) and their corresponding contributions (abundances). When dealing with complex mixed-terrain scenes, such as urban areas, significant challenges arise due to the complexity of the environment. Urban areas feature intricate geometric structures in individual pixels, including diverse 2-D and 3-D structures and the composite use of various building materials, resulting in highly complex scenarios. To address these challenges, this work exploits urban auxiliary information in the framework of an adaptive multitask autoencoder (AE) unmixing model, utilizing graph associations. The framework enhances the information in hyperspectral images by utilizing urban auxiliary data. Specifically, it performs superpixel segmentation to subdivide complex urban environments into simpler units. Subsequently, different AE-based unmixing methods are applied to these segmented results. Graph associations are employed to identify similar blocks in the image, incorporating this additional information into the unmixing process. In the experiments conducted for this work, two hyperspectral unmixing datasets were prepared, along with their corresponding urban auxiliary data. The results demonstrate that the proposed method achieves robust performance, even in complex urban environments.