Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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 (doi: 10.20944/preprints202106.0634.v1). Malik, A.; Waheed, M. Unsupervised Classification of Hyperspectral Images using PCA and K-Means. Preprints 2021, 2021060634 (doi: 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

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