Fatima, W.; Ejaz, I. Hyperspectral Image Classification Using Unsupervised Learning Algorithms. Preprints2021, 2021060706. https://doi.org/10.20944/preprints202106.0706.v1
APA Style
Fatima, W., & Ejaz, I. (2021). Hyperspectral Image Classification Using Unsupervised Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202106.0706.v1
Chicago/Turabian Style
Fatima, W. and Iqra Ejaz. 2021 "Hyperspectral Image Classification Using Unsupervised Learning Algorithms" Preprints. https://doi.org/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
Subject
Environmental and Earth Sciences, Remote Sensing
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.
