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Classification of hyperspectral images based on fused 3D inception and 3D-2D hybrid convolution

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Abstract

A new hyperspectral image classification algorithm based on deep learning is constructed to solve the problems of redundant band information, neglect of local details, and insufficient spatial and spectral feature extraction in hyperspectral image classification tasks. The model uses the improved 3D inception structure as a multi-scale feature extractor to enhance the attention to local information, and 3D convolution mixed with 2D convolution (3D-2D) is used as the main feature extractor to improve the conversion and fusion of spatial and spectral features. In addition, a compression-and-excitation network is used as the connecting mechanism for feature transfer to reduce the redundancy of band information and ultimately to realize the effective classification of hyperspectral images. In this paper, the proposed method was validated on three public datasets (Pavia University, Salinas, and Indian Pines), and the results show that the classification accuracies of the proposed method were 99.75, 99.99, and 98.77%, respectively, which are better than the mainstream methods. These results are of great significance for the performance of hyperspectral image classification tasks.

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Data availability

The datasets analyzed during the current study were derived from the following public domain resources: https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.

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Funding

This work was supported by the Traditional Chinese Medicine Industry Innovation Consortium Project of Gansu Province (No. 22ZD6FA021-5), the Industrial Support Project of Gansu Province (Nos. 2023CYZC-19, 2021CYZC-22, 2021CYZC-01), the Science and Technology Project of Gansu Province (Nos. 23YFFA0074, 22JR5RA137, 22JR5RA151), and Lanzhou Talent Innovation and Entrepreneurship Project (No. 2020-RC-143).

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Authors and Affiliations

  1. Key Laboratory of Atomic and Molecular Physics and Functional Material of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Jingke Shen, Denghong Zhang, Guanghui Dong, Duixiong Sun & Maogen Su

  2. Gansu International Scientific and Technological Cooperation Base of Laser Plasma Spectroscopy, Lanzhou, 730070, Gansu, China

    Denghong Zhang & Maogen Su

  3. Engineering Research Center of Gansu Province for Intelligent Information Technology and Application, Lanzhou, 730070, Gansu, China

    Xiyin Liang

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  1. Jingke Shen
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  2. Denghong Zhang
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  3. Guanghui Dong
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  4. Duixiong Sun
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  5. Xiyin Liang
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Contributions

Jingke Shen wrote the main manuscript text, Duixiong Sun and Xiyin Liang provided the conceptual framework for the article, Jingke Shen and Guanghui Dong discussed issues that arose during the experimental phase of the article, and Denghong Zhang and Maogen Su reviewed the manuscript.

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Correspondence to Denghong Zhang or Maogen Su.

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Shen, J., Zhang, D., Dong, G. et al. Classification of hyperspectral images based on fused 3D inception and 3D-2D hybrid convolution. SIViP 18, 3031–3041 (2024). https://doi.org/10.1007/s11760-023-02968-3

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  • DOI: https://doi.org/10.1007/s11760-023-02968-3

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