Paper Info
Title
Performance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets.
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.
Year
DOI
Venue
2016
10.3390/rs8040295
REMOTE SENSING
Keywords
Field
DocType
cluster validity index,remote sensing,image clustering,cluster number of image
Computer vision,Data set,Cluster validity index,Remote sensing,Multispectral image,Fuzzy logic,Hyperspectral imaging,Artificial intelligence,Cluster analysis,Geology
Journal
Volume
Issue
ISSN
8
4
2072-4292
Citations 
PageRank 
References 
1
0.35
0
Authors
5
Name
Order
Citations
PageRank
Huapeng Li131.72
Shuqing Zhang2306.18
Xiaohui Ding310.69
Ce Zhang44414.01
Pat Dale5182.59