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Remote Sens. 2016, 8(4), 295; doi:10.3390/rs8040295

Performance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets

1
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
4
Environmental Futures Research Institute, School of Environment, Griffith University, Brisbane, QLD 4111, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Guoqing Zhou, Qihao Weng and Prasad S. Thenkabail
Received: 21 December 2015 / Revised: 26 February 2016 / Accepted: 21 March 2016 / Published: 30 March 2016
View Full-Text   |   Download PDF [3842 KB, uploaded 30 March 2016]   |  

Abstract

The number of clusters (i.e., the number of classes) for unsupervised classification has been recognized as an important part of remote sensing image clustering analysis. The number of classes is usually determined by cluster validity indices (CVIs). Although many CVIs have been proposed, few studies have compared and evaluated their effectiveness on remote sensing datasets. In this paper, the performance of 16 representative and commonly-used CVIs was comprehensively tested by applying the fuzzy c-means (FCM) algorithm to cluster nine types of remote sensing datasets, including multispectral (QuickBird, Landsat TM, Landsat ETM+, FLC1, and GaoFen-1) and hyperspectral datasets (Hyperion, HYDICE, ROSIS, and AVIRIS). The preliminary experimental results showed that most CVIs, including the commonly used DBI (Davies-Bouldin index) and XBI (Xie-Beni index), were not suitable for remote sensing images (especially for hyperspectral images) due to significant between-cluster overlaps; the only effective index for both multispectral and hyperspectral data sets was the WSJ index (WSJI). Such important conclusions can serve as a guideline for future remote sensing image clustering applications. View Full-Text
Keywords: cluster validity index; remote sensing; image clustering; cluster number of image cluster validity index; remote sensing; image clustering; cluster number of image
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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. (CC BY 4.0).

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Li, H.; Zhang, S.; Ding, X.; Zhang, C.; Dale, P. Performance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets. Remote Sens. 2016, 8, 295.

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