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A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery

A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery

Title: A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery
Author: Ghamisi, Pedram
Ali, Abder-Rahman
Couceiro, Micael S.
Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Date: 2015
Language: English
Scope: 2447-2456
University/Institute: Háskóli Íslands
University of Iceland
School: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Department: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
ISSN: 1939-1404
Subject: Accuracy; Clustering algorithms; Clustering methods; Hyperspectral imaging; Support vector machines; Training
URI: https://hdl.handle.net/20.500.11815/62

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P. Ghamisi, A. R. Ali, M. S. Couceiro and J. A. Benediktsson, "A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, pp. 2447-2456, June 2015.


In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes of interest. To overcome this issue and model such conditions, fuzzy techniques that resemble human reasoning have been proposed as alternatives. Fuzzy C-means is the most common fuzzy clustering technique, but its concept is based on a local search mechanism and its convergence rate is rather slow, especially considering high-dimensional problems (e.g., in processing of hyperspectral images). Here, in order to address those shortcomings of hard approaches, a new approach is proposed, i.e., fuzzy C-means which is optimized by fractional order Darwinian particle swarm optimization. In addition, to speed up the clustering process, the histogram of image intensities is used during the clustering process instead of the raw image data. Furthermore, the proposed clustering approach is combined with support vector machine classification to accurately classify hyperspectral images. The new classification framework is applied on two well-known hyperspectral data sets; Indian Pines and Salinas. Experimental results confirm that the proposed swarm-based clustering approach can group hyperspectral images accurately in a time-efficient manner compared to other existing clustering techniques.


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