Collaborative optimization of spatial-spectrum parallel convolutional network (CO-PCN) for hyperspectral image classification

The deep learning model has demonstrated excellent performance in the fitting of data and knowledge. For hyperspectral images, accurate classification is still difficult in the case of limited samples and high-dimensional relevance. In this paper, we propose a collaborative optimization parallel convolution network consisting of 3D-2D CNN for hyperspectral image classification. One branch of the parallel network is a 3D-CNN consisting of three blocks for extracting spectrum features and spectrum correlation. The three blocks include a 3D bottleneck block (convolution), SEblock (attention), and a spatial-spectrum convolution module. Secondly, the diverse Region feature extraction network is employed as a spatial-spectrum feature computing module. Finally, the classification predictions from the two branches are fused to obtain the classification results. By comparing the experimental results conducted on three datasets, the proposed method performs significantly better than the SOTA methods in comparison and has better generalization capability.

Automated summary

In this paper, a collaborative optimization parallel convolution network consisting of 3D-2D CNN is proposed for accurate classification of hyperspectral images. The experimental results show that this method outperforms the state-of-the-art methods and has better generalization capability.