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Classification of hyperspectral imagery using spectrally partitioned HyperUnet

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Abstract

Classification is one of the forefront research areas in hyperspectral image processing. The large intra-class and small inter-class variance in the pixel values of objects of interest still poses challenges in classification task. The huge dimension and a minimal number of labeled information further add challenges in the case of hyperspectral image classification. Therefore, in the present research, a novel architecture is conceived which is inspired by the U-net architecture along with spectral partitioning. The proposed architecture (HyperUnet) mainly addresses the broader issue of classification of hyperspectral images by classifying each pixel. The performance of the proposed model is evaluated on two benchmark datasets and compared with existing U-net-based models. The overall classification accuracy obtained in experiments is more than 93% which is better than the other compared methods in the same field.

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Notes

  1. http://www.ehu.eus/ccwintco/index.php/HyperspectralRemoteSensingScenes.

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Acknowledgements

The authors acknowledge the support of CGM RCs, NRSC, ISRO, GM, RRSC-East, NRSC and Head (Applications), RRSC-East, NRSC, for carrying out the work. The authors also acknowledge the collaboration extended by VC, MAKAUT, toward the work.

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Authors and Affiliations

  1. Regional Remote Sensing Centre-East, ISRO, Kolkata, India

    Arati Paul

  2. Maulana Abul Kalam Azad University of Technology, Haringhata, West Bengal, India

    Sanghamita Bhoumik

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  1. Arati Paul
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  2. Sanghamita Bhoumik
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Correspondence to Arati Paul.

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Paul, A., Bhoumik, S. Classification of hyperspectral imagery using spectrally partitioned HyperUnet. Neural Comput & Applic 34, 2073–2082 (2022). https://doi.org/10.1007/s00521-021-06532-3

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