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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Hyperspectral Image Classification Using Unsupervised Algorithms

Author 1: Sahar A. El_Rahman

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2016.070425

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 4, 2016.

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Abstract: Hyperspectral Imaging (HSI) is a process that results in collected and processed information of the electromagnetic spectrum by a specific sensor device. It’s data provide a wealth of information. This data can be used to address a variety of problems in a number of applications. Hyperspectral Imaging classification assorts all pixels in a digital image into groups. In this paper, unsupervised hyperspectral image classification algorithms used to obtain a classified hyperspectral image. Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) algorithm and K-Means algorithm are used. Applying two algorithms on Washington DC hyperspectral image, USA, using ENVI tool. In this paper, the performance was evaluated on the base of the accuracy assessment of the process after applying Principle Component Analysis (PCA) and K-Means or ISODATA algorithm. It is found that, ISODATA algorithm is more accurate than K-Means algorithm. Since The overall accuracy of classification process using K-Means algorithm is 78.3398% and The overall accuracy of classification process using ISODATA algorithm is 81.7696%. Also the processing time increased when the number of iterations increased to get the classified image.

Keywords: hyperspectral imaging; unsupervised classification; K-Means algorithm; ISODATA algorithm; ENVI

Sahar A. El_Rahman, “Hyperspectral Image Classification Using Unsupervised Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 7(4), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070425

@article{El_Rahman2016,
title = {Hyperspectral Image Classification Using Unsupervised Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070425},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070425},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {4},
author = {Sahar A. El_Rahman}
}


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