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. Preprints2021, 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
Malik, A.; Waheed, M. Unsupervised Classification of Hyperspectral Images using PCA and K-Means. Preprints2021, 2021060634. https://doi.org/10.20944/preprints202106.0634.v1
APA Style
Malik, A., & Waheed, M. (2021). Unsupervised Classification of Hyperspectral Images using PCA and K-Means. Preprints. https://doi.org/10.20944/preprints202106.0634.v1
Chicago/Turabian Style
Malik, A. and Mamoona Waheed. 2021 "Unsupervised Classification of Hyperspectral Images using PCA and K-Means" Preprints. 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.
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.
