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

  • Muhamad Soleh Faculty of Computer Science, Universitas Indonesia
  • Aniati Murni Arymurthy Faculty of Computer Science, Universitas Indonesia
  • Sesa Wiguna Geography, The University of Auckland – New Zealand
DOI: https://doi.org/10.21609/jiki.v11i2.623
Keywords: Change Detection, Multistage Clustering, Disaster Recovery Planning, Fuzzy C Means, K-Means

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.
Published
2018-06-29
How to Cite
Soleh, M., Arymurthy, A. M., & Wiguna, S. (2018). CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING. Jurnal Ilmu Komputer Dan Informasi, 11(2), 110-117. https://doi.org/10.21609/jiki.v11i2.623
Issue
Vol. 11 No. 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Section
Articles

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