NDLI: Cluster centres determination based on KD tree in K-Means clustering for area change detection

Content Provider	IEEE Xplore Digital Library
Author	Sirait, P. Arymurthy, A.M.
Copyright Year	2010
Description	Author affiliation: Departement Of Informatics, School of Informatics and Computer Management, Mikroskil, Medan - Indonesia (Sirait, P.) \|\| Faculty of Computer Science, University Of Indonesia, Jakarta - Indonesia (Arymurthy, A.M.)
Abstract	This paper presents a study on area change detection applications based on remote sensing data. The crucial parts of the process are in selecting the optimal combination of bands and in the image clustering process, so that we could obtain the object regions correctly. The proposed methodology consists of the following steps: (i) image band selection using Optimum Index Factor; (ii) K-Means clustering where their cluster centres are determined by K-D tree; and (iii) detecting area changes. For experiment purposes, temporal images that are registered to each other are required. The image registration is done by matching several ground control points between two or more temporal images. The experiments have used the images of Kalimantan, with the size of 512×512 pixels, and are recorded in the years of 2002 and 2009. The experiments have used both random approach and K-D tree based approach for determining the initial cluster centres in the clustering process. The experimental results show that the K-D-tree based approach gave better results than the random approach in terms of the similarity measure of the clusters' members.
Starting Page	1
Ending Page	7
File Size	893920
Page Count	7
File Format	PDF
ISBN	9781424493357
e-ISBN	9786029747911
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-02
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	University Sains Malaysia
Subject Keyword	Pixel Covariance matrix Correlation Mathematical model Equations Clustering algorithms Image classification Change Detection Optimum Index Factor K-D Tree K-Means Clustering Similarity Criteria
Content Type	Text
Resource Type	Article

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