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

Pahala Sirait, Aniati Murni Arymurthy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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 512512 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.

Original languageEnglish
Title of host publication2010 International Conference on Distributed Frameworks for Multimedia Applications, DFmA 2010
Publication statusPublished - 1 Dec 2010
Event2010 International Conference on Distributed Frameworks for Multimedia Applications, DFmA 2010 - Yogyakarta, Indonesia
Duration: 2 Aug 20103 Aug 2010

Publication series

Name2010 International Conference on Distributed Frameworks for Multimedia Applications, DFmA 2010

Conference

Conference2010 International Conference on Distributed Frameworks for Multimedia Applications, DFmA 2010
Country/TerritoryIndonesia
CityYogyakarta
Period2/08/103/08/10

Keywords

  • Change Detection
  • K-D Tree
  • K-Means Clustering
  • Optimum Index Factor
  • Similarity Criteria

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