基于双注意力卷积神经网络的高光谱图像解混

基于双注意力卷积神经网络的高光谱图像解混 In this article, we design an end-to-end hyperspectral unmixing method based on dual attention convolutional neural network (DACN), 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