Local similarity measure for landslide detection and identification in comparison with the image differencing method
In this article, a new simple method of landslide detection and identification is proposed. It is based on the use of local mutual information and image thresholding. A binary change image is then produced. Connected component analysis is used to identify the connected regions. Landslides are identi...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Taylor & Francis
2010
|
| _version_ | 1825945629574037504 |
|---|---|
| author | Bejo, Siti Khairunniza Petrou, Maria Ganas, Athanassios |
| author_facet | Bejo, Siti Khairunniza Petrou, Maria Ganas, Athanassios |
| author_sort | Bejo, Siti Khairunniza |
| collection | UPM |
| description | In this article, a new simple method of landslide detection and identification is proposed. It is based on the use of local mutual information and image thresholding. A binary change image is then produced. Connected component analysis is used to identify the connected regions. Landslides are identified as the largest connected regions in this image. Mathematical morphology is used to approximate the landslide region. This method is simple and suitable for the detection of large changed regions where the ratio of the unchanged to changed pixels in the image is approximately one to a few tens. Compared to the image differencing method, this method gives more reliable results. |
| first_indexed | 2024-03-06T07:34:28Z |
| format | Article |
| id | upm.eprints-15481 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T07:34:28Z |
| publishDate | 2010 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | upm.eprints-154812016-01-20T06:45:16Z http://psasir.upm.edu.my/id/eprint/15481/ Local similarity measure for landslide detection and identification in comparison with the image differencing method Bejo, Siti Khairunniza Petrou, Maria Ganas, Athanassios In this article, a new simple method of landslide detection and identification is proposed. It is based on the use of local mutual information and image thresholding. A binary change image is then produced. Connected component analysis is used to identify the connected regions. Landslides are identified as the largest connected regions in this image. Mathematical morphology is used to approximate the landslide region. This method is simple and suitable for the detection of large changed regions where the ratio of the unchanged to changed pixels in the image is approximately one to a few tens. Compared to the image differencing method, this method gives more reliable results. Taylor & Francis 2010-07 Article PeerReviewed Bejo, Siti Khairunniza and Petrou, Maria and Ganas, Athanassios (2010) Local similarity measure for landslide detection and identification in comparison with the image differencing method. International Journal of Remote Sensing, 31 (23). pp. 6033-6045. ISSN 0143-1161 http://www.tandfonline.com/doi/abs/10.1080/01431160903376365 10.1080/01431160903376365 |
| spellingShingle | Bejo, Siti Khairunniza Petrou, Maria Ganas, Athanassios Local similarity measure for landslide detection and identification in comparison with the image differencing method |
| title | Local similarity measure for landslide detection and identification in comparison with the image differencing method |
| title_full | Local similarity measure for landslide detection and identification in comparison with the image differencing method |
| title_fullStr | Local similarity measure for landslide detection and identification in comparison with the image differencing method |
| title_full_unstemmed | Local similarity measure for landslide detection and identification in comparison with the image differencing method |
| title_short | Local similarity measure for landslide detection and identification in comparison with the image differencing method |
| title_sort | local similarity measure for landslide detection and identification in comparison with the image differencing method |
| work_keys_str_mv | AT bejositikhairunniza localsimilaritymeasureforlandslidedetectionandidentificationincomparisonwiththeimagedifferencingmethod AT petroumaria localsimilaritymeasureforlandslidedetectionandidentificationincomparisonwiththeimagedifferencingmethod AT ganasathanassios localsimilaritymeasureforlandslidedetectionandidentificationincomparisonwiththeimagedifferencingmethod |