Multibranch 3D-Dense Attention Network for Hyperspectral Image Classification

The convolutional neural network (CNN) is widely used in the task of hyperspectral image (HSI) classification. However, for the HSI of three-dimensional characteristics, the 2D CNN-based methods will result in losing spatial-spectral information. To solve this problem, this paper proposes a multi-branch 3D-densely connected network for HSI classification. This network is able to reuse features to fully exploit the shallow spatial-spectral information of HSI. Meanwhile, the convolutional kernels of different sizes are used to extract multi-scale spatial-spectral features. Subsequently, spatial attention mechanisms are used to emphasize spatial features and increase the diversity of features. By introducing global average pooling instead of a fully connected layer, the number of parameters in the whole network will be reduced. In order to verify the performance of the proposed method, the experiment results conducted in the Indian Pines, the University of Pavia, Salinas Valley and Houston 2013 datasets show that the proposed method is better than the state-of-the-art methods.

Automated summary

This paper proposes a multi-branch 3D-densely connected network for HSI classification to address the issue of losing data due to the three-dimensional characteristics. The network is able to fully exploit the spatial-spectral information of HSI and extract multi-scale features using convolutional kernels of different sizes and spatial attention mechanisms. The number of parameters is reduced by introducing global average pooling instead of a fully connected layer.