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...

Full description

Bibliographic Details
Main Authors: Bejo, Siti Khairunniza, Petrou, Maria, Ganas, Athanassios
Format: Article
Published: Taylor & Francis 2010
Online Access:http://psasir.upm.edu.my/id/eprint/15481/
_version_ 1848842689504608256
author Bejo, Siti Khairunniza
Petrou, Maria
Ganas, Athanassios
author_facet Bejo, Siti Khairunniza
Petrou, Maria
Ganas, Athanassios
author_sort Bejo, Siti Khairunniza
building UPM Institutional Repository
collection Online Access
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 2025-11-15T08:03:07Z
format Article
id upm-15481
institution Universiti Putra Malaysia
institution_category Local University
last_indexed 2025-11-15T08:03:07Z
publishDate 2010
publisher Taylor & Francis
recordtype eprints
repository_type Digital Repository
spelling upm-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
url http://psasir.upm.edu.my/id/eprint/15481/
http://psasir.upm.edu.my/id/eprint/15481/
http://psasir.upm.edu.my/id/eprint/15481/