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

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Abstract

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.

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Funding

The work was funded by Natural Science Foundation of China to Haifeng Sima with grant number 61602157, Science and Technology Department of Henan Province with grant number 202102210167, and by Doctoral Foundation under Grant B2016-37.

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Author notes

  1. Haifeng Sima, Feng Gao have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer science and technology, Henan Polytechnic University, ShiJi Road, Jiaozuo, 454000, China

    Haifeng Sima, Feng Gao & Junding Sun

  2. School of Computing and mathematical Science, University of Leicester, Leicester, LE1 7RH, UK

    Yudong Zhang

  3. School of System Science, Beijing Normal University, Haidian, Beijing, 100875, China

    Ping Guo

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  1. Haifeng Sima
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  2. Feng Gao
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Correspondence to Junding Sun.

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Sima, H., Gao, F., Zhang, Y. et al. Collaborative optimization of spatial-spectrum parallel convolutional network (CO-PCN) for hyperspectral image classification. Int. J. Mach. Learn. & Cyber. 14, 2353–2366 (2023). https://doi.org/10.1007/s13042-022-01767-5

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