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DesU-NetAM: optimized DenseU-Net with attention mechanism for hyperspectral image classification

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Abstract

The utilization of hyperspectral images (HSI) is expanding rapidly with the advancement of remote sensing technology. Accurately categorizing ground features using HSI is a crucial research topic that has garnered considerable interest. The high dimensional space, numerous spectral bands, and lack of labeled training data make categorizing hyperspectral images difficult. We provide a novel hyperspectral image categorization approach based on DenseU-Net to address these issues. The data is first normalized by separating the maximum value of the entire set of data by the average intensity of each pixel. Then we also propose an attention mechanism network. Because spectral and spatial data are extracted independently, there may be less interference between the two types of features in this network. The retrieved spectral and spatial data are integrated to categorize the data. Finally, DenseU-Net method parameters are adjusted using the Tuna Swarm Optimization (TSO) algorithm, which has a global search capability. This optimized DenseU-Net is then utilized to manage a hyperspectral image categorization approach effectively. Three standard HSI datasets utilized for experiments were acquired by various sensors at different acquisition times and used for classification studies. The comparison findings show that the proposed approach outperforms other deep learning methods models in classification effectiveness.

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Acknowledgements

We declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, B V Raju Institute of Technology, Vishnupur, Narsapur, Telangana, 502313, India

    K. Balaji, Veeramachaneni Nirosha & G. Prathyusha

  2. Department of Electronics and Communication Engineering, Vignan’s Nirula Institute of Technology and Science for Women, Peda Palakaluru, Guntur, Andhra Pradesh, 522009, India

    S. Yallamandaiah

  3. Department of Computer Science and Engineering, R. V. R & J. C College of Engineering, Chowdavaram, Guntur, Andhra Pradesh, 522019, India

    Sajja Karthik

  4. Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Sree Sainath Nagar, Tirupati, Andhra Pradesh, 517102, India

    V. Siva Prasad

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  1. K. Balaji
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  3. S. Yallamandaiah
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Contributions

KB, VN: conceptualization, methodology, software, formal analysis, investigation, resources, writing – original draft, writing - review & editing, visualization. YS, SK: review & editing. VSP, GP: investigation, resources, data curation, writing – original draft, writing - review & editing.

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Correspondence to K. Balaji.

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Balaji, K., Nirosha, V., Yallamandaiah, S. et al. DesU-NetAM: optimized DenseU-Net with attention mechanism for hyperspectral image classification. Int. j. inf. tecnol. 15, 3761–3777 (2023). https://doi.org/10.1007/s41870-023-01386-5

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  • DOI: https://doi.org/10.1007/s41870-023-01386-5

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