Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 14, Issue 7, Pages 2353-2366Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-022-01767-5
Keywords
Hyperspectral image (HSI) classification; Deep learning; Convolutional neural network (CNN); Collaborative learning
Categories
Ask authors/readers for more resources
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.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available