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CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 11 No. 2 (2018) · pp. 110–117

DOI
10.21609/jiki.v11i2.623
ISSN
2502-9274, 2088-7051
Published
2018-06-29
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance.

Keywords

Collaboration network of the authors

32 authors · 68 co-authorship links · 28 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Indonesia
  • Mercu Buana University
  • Universitas Gadjah Mada
  • University of Auckland
  • University of Reading
  • Universitas Stikubank
  • RWTH Aachen University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ruli ManurungAffiliation unknown2
Ida NurhaidaMercu Buana University2
Remmy A. M. ZenMercu Buana University2
Hong WeiUniversity of Reading2
Agus HarjokoUniversitas Gadjah Mada2
Edi WinarkoUniversitas Gadjah Mada2
Edy WinarnoUniversitas Stikubank2
Edward CitrahadiAffiliation unknown1
Tieta AntarestiUniversity of Indonesia1
Vina AyumiMercu Buana University1
L. M. Rasdi RereUniversity of Indonesia1
Mohamad Ivan FananyUniversity of Indonesia1
Yohanes GultomUniversity of Indonesia1
Rian Josua MasikomeRWTH Aachen University1
Muhammad Hafizhuddin HilmanThe University of Melbourne1
Grafika JatiUniversity of Indonesia1
Yuni ArtiUniversity of Indonesia1
Hans Timothy WijayaUniversity of Indonesia1
Juan Thomas WijayaUniversity of Indonesia1
Yohanes Fridolin HestrioUniversity of Indonesia1
Bayu Satria PersadaUniversity of Indonesia1
Frederic Morado SaragihUniversity of Indonesia1
Muhammad Yusuf KardawiUniversity of Indonesia1
Wisnu Aryo JatmikoUniversity of Indonesia1
Herwanto HerwantoAffiliation unknown1
Achmad Nizar HidayantoUniversity of Indonesia1
Hisyam FahmiUniversitas Islam Negeri Maulana Malik Ibrahim1
Devi FitrianahMercu Buana University1
Pang‐Ning TanMichigan State University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING</dc:title>
	<dc:creator>Soleh, Muhamad</dc:creator>
	<dc:creator>Arymurthy, Aniati Murni</dc:creator>
	<dc:creator>Wiguna, Sesa</dc:creator>
	<dc:subject xml:lang="en-US">Change Detection</dc:subject>
	<dc:subject xml:lang="en-US">Multistage Clustering</dc:subject>
	<dc:subject xml:lang="en-US">Disaster Recovery Planning</dc:subject>
	<dc:subject xml:lang="en-US">Fuzzy C Means</dc:subject>
	<dc:subject xml:lang="en-US">K-Means</dc:subject>
	<dc:description xml:lang="en-US">Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2018-06-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/623</dc:identifier>
	<dc:identifier>10.21609/jiki.v11i2.623</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 11 No. 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 110-117</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v11i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/623/400</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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