Abstract: Abstract: A new neural network, called DenseNet-Attention (DANet), was proposed in this paper for hyperspectral images classification to solve the problems of small sample quantity, insufficient features extraction, and indiscriminating contribution of the extracted features. First, it employed the three-dimensional convolution kernel to simultaneously extract both spectral and spatial features. Meanwhile, due to its dense blocks, DenseNet can not only fully extract more robust features, but reduce a large number of parameters. Second, the self-attention mechanism was added to the dense block as a module. Before the extracted feature was passed into the next layer of network, the weight was assigned to the feature according to its contribution through this model, thus strengthening the representation of the feature with ground object information. DANet was an end-to-end deep learning framework, which took the neighborhood block of the original hyperspectral image as an input without any preprocessing. Comparative experiments on Indian Pines and Pavia University datasets show that the classification accuracy of the network model proposed in this paper can reach 99.43% and 99.99% respectively, effectively enhancing the classification accuracy of hyperspectral images.

Key words: Keywords: three-dimensional convolution, hyperspectral remote sensing image classification, DenseNet, self-attenrion, skip connect