Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Hyperspectral Image Classification Using Unsupervised Learning Algorithms

Version 1 : Received: 27 June 2021 / Approved: 29 June 2021 / Online: 29 June 2021 (12:56:59 CEST)

How to cite: Fatima, W.; Ejaz, I. Hyperspectral Image Classification Using Unsupervised Learning Algorithms. Preprints 2021, 2021060706 (doi: 10.20944/preprints202106.0706.v1). Fatima, W.; Ejaz, I. Hyperspectral Image Classification Using Unsupervised Learning Algorithms. Preprints 2021, 2021060706 (doi: 10.20944/preprints202106.0706.v1).

Abstract

Hyperspectral image (HSI) classification is a mechanism of analyzing differentiated land cover in remotely sensed hyperspectral images. In the last two decades, a number of different types of classification algorithms have been proposed for classifying hyperspectral data. These algorithms include supervised as well as unsupervised methods. Each of these algorithms has its own limitations. In this research, three different types of unsupervised classification methods are used to classify different datasets i-e Pavia Center, Pavia University, Cuprite, Moffett Field. The main objective is to assess the performance of all three classifiers K-Means, Spectral Matching, and Abundance Mapping, and observing their applicability on different datasets. This research also includes spectral feature extraction for hyperspectral datasets.

Keywords

Hyperspectral Images, Classification, K means, Spectral Matching, Abundance Estimation

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