Article
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Unsupervised Classification of Hyperspectral Images using PCA and K-Means
Version 1
: Received: 23 June 2021 / Approved: 28 June 2021 / Online: 28 June 2021 (10:01:41 CEST)
How to cite: Malik, A.; Waheed, M. Unsupervised Classification of Hyperspectral Images using PCA and K-Means. Preprints 2021, 2021060634. https://doi.org/10.20944/preprints202106.0634.v1 Malik, A.; Waheed, M. Unsupervised Classification of Hyperspectral Images using PCA and K-Means. Preprints 2021, 2021060634. https://doi.org/10.20944/preprints202106.0634.v1
Abstract
The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.
Keywords
Hyperspectral image; HSI; PCA; K-means clustering; unsupervised; classification; bands; satellite; ROSIS; AVIRIS
Subject
Computer Science and Mathematics, Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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