Hyperspectral unmixing method based on dual-branch multiscale residual attention network.

We propose a solution to the issue of hyperspectral unmixing methods that only consider local spectral–spatial information of the pixel level or pixel block. The proposed method is a two-stage convolutional autoencoder network that takes into account global spatial context information. In stage-I, the parallel dual-branch module is dedicated to extracting multi-scale spatial and spectral features. The extracted spatial–spectral prior information is then propagated from stage-I to stage-II to assist in the extraction of joint spectral–spatial features. The proposed method also uses a spectral–spatial attention residual module to refine spectral–spatial features into distinct deep spectral–spatial features and suppress irrelevant redundant features. We validate the proposed method using synthetic and real datasets. It is found that the proposed method outperforms existing unmixing methods in terms of endmembers extraction and abundance estimation. The source code for the proposed model will be made public in the Github repository available at https://github.com/xzw001212/two-stage-DBMRANet. [ABSTRACT FROM AUTHOR]