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

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.

• Publication year

  2018

• Venue

  Jurnal Ilmu Komputer dan Informasi

• Publication date

  2018-06-29

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.21609/jiki.v11i2.623
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Change Detection in Multispectral Landsat Images Using Multiobjective Evolutionary Algorithm

  2017cited by this paper
• Self-adaptive hybrid PSO-GA method for change detection under varying contrast conditions in satellite images

  2016cited by this paper
• Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet

  2016cited by this paper
• Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

  2016cited by this paper
• Gabor Feature Based Unsupervised Change Detection of Multitemporal SAR Images Based on Two-Level Clustering

  2015cited by this paper
• Fuzzy Clustering With a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images

  2014cited by this paper
• Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images

  2013cited by this paper
• A New Approach to Change Detection in Multispectral Images by Means of ERGAS Index

  2013cited by this paper
• Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering

  2012cited by this paper
• Change Detection in Satellite Images Using a Genetic Algorithm Approach

  2010cited by this paper
• Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering

  2009influential reference
• A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis

  2007cited by this paper
• Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery

  2005cited by this paper
• Image change detection algorithms: a systematic survey

  2005cited by this paper
• Image quality assessment: from error visibility to structural similarity

  2004cited by this paper

• HWA-Net: Hierarchical window aggregate network for cross-resolution remote sensing change detection

  2025cites this paper
• STRATEGY OPTIMIZATION FOR RESPONDING TO PRIMARY, SECONDARY AND RESIDUAL RISKS CONSIDERING COST AND TIME DIMENSIONS IN PETROCHEMICAL PROJECTS

  2022cites this paper
• Student Academic Mark Clustering Analysis and Usability Scoring on Dashboard Development Using K-Means Algorithm and System Usability Scale

  2021cites this paper
• Disaster Recovery Planning: Untapped Success Factor in an Organization

  2020cites this paper