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

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 “NonLocalBlock” 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 “Conv_Block” 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.