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Hyperspectral unmixing is to decompose the mixed pixel into the spectral signatures (endmembers) with their corresponding abundances. However, the ignorance of endmember variability in hyperspectral unmixing results in low performance. To solve this problem, a multi-scale convolution attention network containing endmember unmixing network (EU-Net) and abundance unmixing network, which was called as-the hyperspectral unmixing with multi-scale convolution attention network (HUMSCAN) was proposed in this article. The EU-Net is composed of the variational autoencoder and the multi-scale effective convolution block attention module (MSECBAM), which is combined with the S-VCA pretraining to adaptively extract endmembers at the pixel and subpixel levels. The AU-Net is based on the MSECBAM frame jointed the spectral and spatial attention features. The proposed HUMSCAN method can simultaneously and unsupervisedly extract endmembers and their corresponding abundances, which can improve the accuracy and efficiency of spectral unmixing. The performance of the proposed method is evaluated both on synthetic and real datasets. Experimental results show its superiority in comparison with other state-of-the-art methods.

Hyperspectral unmixing is to decompose the mixed pixel into the spectral signatures (endmembers) with their corresponding abundances. However, the ignorance of endmember variability in hyperspectral unmixing results in low performance. To solve this problem, a multi-scale convolution attention network containing endmember unmixing network (EU-Net) and abundance unmixing network, which was called as-the hyperspectral unmixing with multi-scale convolution attention network (HUMSCAN) was proposed in this article. The EU-Net is composed of the variational autoencoder and the multi-scale effective convolution block attention module (MSECBAM), which is combined with the S-VCA pretraining to adaptively extract endmembers at the pixel and subpixel levels. The AU-Net is based on the MSECBAM frame jointed the spectral and spatial attention features. The proposed HUMSCAN method can simultaneously and unsupervisedly extract endmembers and their corresponding abundances, which can improve the accuracy and efficiency of spectral unmixing. The performance of the proposed method is evaluated both on synthetic and real datasets. Experimental results show its superiority in comparison with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]