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 detect...
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| Format: | Article |
| Language: | English |
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Universitas Indonesia
2018-06-01
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| Series: | Jurnal Ilmu Komputer dan Informasi |
| Subjects: | |
| Online Access: | http://jiki.cs.ui.ac.id/index.php/jiki/article/view/623 |
| _version_ | 1818967476147322880 |
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| author | Muhamad Soleh Aniati Murni Arymurthy Sesa Wiguna |
| author_facet | Muhamad Soleh Aniati Murni Arymurthy Sesa Wiguna |
| author_sort | Muhamad Soleh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T13:49:24Z |
| format | Article |
| id | doaj.art-c7ff351915af4d6f8cad980d74eafad7 |
| institution | Directory Open Access Journal |
| issn | 2088-7051 2502-9274 |
| language | English |
| last_indexed | 2024-12-20T13:49:24Z |
| publishDate | 2018-06-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | Jurnal Ilmu Komputer dan Informasi |
| spelling | doaj.art-c7ff351915af4d6f8cad980d74eafad72022-12-21T19:38:33ZengUniversitas IndonesiaJurnal Ilmu Komputer dan Informasi2088-70512502-92742018-06-0111211011710.21609/jiki.v11i2.623249CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNINGMuhamad Soleh0Aniati Murni Arymurthy1Sesa Wiguna2Faculty of Computer Science, Universitas IndonesiaFaculty of Computer Science, Universitas IndonesiaGeography, The University of Auckland – New ZealandChange 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.http://jiki.cs.ui.ac.id/index.php/jiki/article/view/623Change DetectionMultistage ClusteringDisaster Recovery PlanningFuzzy C MeansK-Means |
| spellingShingle | Muhamad Soleh Aniati Murni Arymurthy Sesa Wiguna CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING Jurnal Ilmu Komputer dan Informasi Change Detection Multistage Clustering Disaster Recovery Planning Fuzzy C Means K-Means |
| title | CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING |
| title_full | CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING |
| title_fullStr | CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING |
| title_full_unstemmed | CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING |
| title_short | CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING |
| title_sort | change detection in multi temporal images using multistage clustering for disaster recovery planning |
| topic | Change Detection Multistage Clustering Disaster Recovery Planning Fuzzy C Means K-Means |
| url | http://jiki.cs.ui.ac.id/index.php/jiki/article/view/623 |
| work_keys_str_mv | AT muhamadsoleh changedetectioninmultitemporalimagesusingmultistageclusteringfordisasterrecoveryplanning AT aniatimurniarymurthy changedetectioninmultitemporalimagesusingmultistageclusteringfordisasterrecoveryplanning AT sesawiguna changedetectioninmultitemporalimagesusingmultistageclusteringfordisasterrecoveryplanning |