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A Pre-processing framework for spectral classification of hyperspectral images

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Abstract

Classification of Hyperspectral images is mostly based on the spectral-spatial features in existing classification techniques. The captured Hyperspectral images from satellites may contain some noisy bands due to water absorption. The process of radiometric and atmospheric corrections leads to the removal of useful bands present in the acquired HSI. In this paper, a novel framework is proposed in which interpolation is used to accommodate the loss of noisy bands. Further, the extraction of hybrid features is performed using PCA and LPP to preserve spatial information, and these features are passed as input to the machine learning models. The proposed framework is compared with the existing spectral-spatial and spectral based frameworks by using the standard datasets-Indian Pines, Salinas, Pavia University, and Kennedy Space Centre. The accuracy of the classification is increased significantly when the proposed framework is blended with state-of-art classifiers.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bennett University, Greater Noida, Uttar Pradesh, India

    Simranjit Singh

  2. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology Patiala, Patiala, Punjab, India

    Singara Singh Kasana

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  1. Simranjit Singh
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  2. Singara Singh Kasana
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Correspondence to Simranjit Singh.

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Singh, S., Kasana, S.S. A Pre-processing framework for spectral classification of hyperspectral images. Multimed Tools Appl 80, 243–261 (2021). https://doi.org/10.1007/s11042-020-09180-2

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  • DOI: https://doi.org/10.1007/s11042-020-09180-2

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