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Deep learning-based methods based on convolutional neural networks (CNNs) have demonstrated remarkable performance in hyperspectral image (HSI) classification. Most of these approaches are only based on 2-D CNN or 3-D CNN. It is dramatic from the literature that using just 2-D CNN may result in missing channel relationship information, and using just 3-D CNN may make the model very complex. Moreover, the existing network models do not pay enough attention to extracting spectral-spatial correlation information. To address these issues, we propose a deep collaborative attention network for HSI classification by combining 2-D CNN, and 3-D CNN (CACNN). Specifically, we first extract spectral-spatial features by using 2-D CNN, and 3-D CNN, respectively, and then use a &#x201C;NonLocalBlock&#x201D; to combine these two kinds of features. This block serves as a typical spatial attention mechanism, and makes salient features be emphasized. Then, we propose a &#x201C;Conv_Block&#x201D; that is similar to the lightweight dense block to extract correlation information contained in the feature maps. Finally, we consider a deep multilayer feature fusion strategy, and thereby combine the features of different hierarchical layers to extract the strong correlated spectral-spatial information among them. To test the performance of CACNN approach, several experiments are performed on four well-known HSIs. The results are compared with the state-of-the-art approaches, and satisfactory performance is obtained by our proposed method. The code of CACNN method is available on Dr. J. Liu's GitHub.

Deep Collaborative Attention Network for Hyperspectral Image Classification by Combining 2-D CNN and 3-D CNN