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A 3D-convolutional-autoencoder embedded Siamese-attention-network for classification of hyperspectral images

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Abstract

The classification of hyperspectral images (HSI) into categories that correlate to various land cover sorts such as water bodies, agriculture and urban areas, has gained significant attention in research due to its wide range of applications in fields, such as remote sensing, computer vision, and more. Supervised deep learning networks have demonstrated exceptional performance in HSI classification, capitalizing on their capacity for end-to-end optimization and leveraging their strong potential for nonlinear modeling. However, labelling HSIs, on the other hand, necessitates extensive domain knowledge and is a time-consuming and labour-intensive exercise. To address this issue, the proposed work introduces a novel semi-supervised network constructed with an autoencoder, Siamese action, and attention layers that achieves excellent classification accuracy with labelled limited samples. The proposed convolutional autoencoder is trained using the mass amount of unlabelled data to learn the refinement representation referred to as 3D-CAE. The added Siamese network improves the feature separability between different categories and attention layers improve classification by focusing on discriminative information and neglecting the unimportant bands. The efficacy of the proposed model’s performance was assessed by training and testing on both same-domain as well as cross-domain data and found to achieve 91.3 and 93.6 for Indian Pines and Salinas, respectively.

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Data availability

The hyperspectral datasets are open and available on https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes

Code availability

The code used for the experiments in this research project is available upon request via mail.

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Funding

No specific grant was received for this research from any public, commercial, or non-profit funding agency.

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Authors and Affiliations

  1. Department of Computer Science, Delhi Technological University, Delhi, India

    Pallavi Ranjan & Rajeev Kumar

  2. Department of Computer Applications, Kurukshetra University, Kurukshetra, India

    Ashish Girdhar

Authors
  1. Pallavi Ranjan
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  2. Rajeev Kumar
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  3. Ashish Girdhar
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Contributions

Pallavi Ranjan conceived the idea, performed implementation, conducted experiments, and wrote the original manuscript. Rajeev Kumar performed writing—review and editing. Ashish Girdhar identified the problem and provided guidance throughout the project.

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Correspondence to Rajeev Kumar.

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Ranjan, P., Kumar, R. & Girdhar, A. A 3D-convolutional-autoencoder embedded Siamese-attention-network for classification of hyperspectral images. Neural Comput & Applic 36, 8335–8354 (2024). https://doi.org/10.1007/s00521-024-09527-y

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  • DOI: https://doi.org/10.1007/s00521-024-09527-y

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