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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1458978
This article is part of the Research Topic Harnessing Machine Learning to Decode Plant-Microbiome Dynamics for Sustainable Agriculture View all 14 articles

SSATNet: Spectral-spatial attention transformer for hyperspectral corn image classification

Provisionally accepted
Bin Wang Bin Wang 1*Gongchao Chen Gongchao Chen 2*Juan Wen Juan Wen 3*Songlin Jin Songlin Jin 2Yan Li Yan Li 4*Ling Zhou Ling Zhou 2*Weidong Zhang Weidong Zhang 2
  • 1 School of Life Sciences, Henan Institute of Science and Technology, Xinxiang, China
  • 2 School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
  • 3 School of Art, Henan University of Economic and Law, Zhengzhou, Henan, China
  • 4 School of Software, Henan Institute of Science and Technology, Xinxiang, China

The final, formatted version of the article will be published soon.

    Hyperspectral images are rich in spectral and spatial information, providing a detailed and comprehensive description of objects, which makes hyperspectral image analysis technology essential in intelligent agriculture. With various corn seed varieties exhibiting significant internal structural differences, accurate classification is crucial for planting, monitoring, and consumption.However, due to the large volume and complex features of hyperspectral corn image data, existing methods often fall short in feature extraction and utilization, leading to low classification accuracy.To address these issues, this paper proposes a spectral-spatial attention transformer network (SSATNet) for hyperspectral corn image classification. Specifically, SSATNet utilizes 3D and 2D convolutions to effectively extract local spatial, spectral, and textural features from the data while incorporating spectral and spatial morphological structures to understand the internal structure of the data better. Additionally, a transformer encoder with cross-attention extracts and refines feature information from a global perspective. Finally, a classifier generates the prediction results.Compared to existing state-of-the-art classification methods, our model performs better on the hyperspectral corn image dataset, demonstrating its effectiveness.

    Keywords: Corn identification, hyperspectral image classification, deep learning, morphology, Image clasification

    Received: 03 Jul 2024; Accepted: 13 Dec 2024.

    Copyright: © 2024 Wang, Chen, Wen, Jin, Li, Zhou and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Bin Wang, School of Life Sciences, Henan Institute of Science and Technology, Xinxiang, China
    Gongchao Chen, School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
    Juan Wen, School of Art, Henan University of Economic and Law, Zhengzhou, 450002, Henan, China
    Yan Li, School of Software, Henan Institute of Science and Technology, Xinxiang, China
    Ling Zhou, School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.