In recent years, deep learning has been widely used in the field of hyperspectral unmixing (HU), but the current HU
method lacks the comprehensive application of spectral-spatial information. Therefore, an end-to-end HU method based on dual attention convolutional neural network (DACN) is proposed in this paper, which adds two types of attention modules on the basis of feature extraction by CNN, and models the semantic information on spectral-spatial dimensions to adaptively fuse local and global features. Furthermore, Layer normalization and Maxpooling are used on DACN to avoid over fitting. The evaluation of the complete performance is carried out on two hyperspectral datasets: Jasper Ridge and Urban. Compared with that of the existing method, our method can extract spectralspatial feature information more effectively, and the precision is improved significantly.