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Semiautomatic Object-Oriented Landslide Recognition Scheme From Multisensor Optical Imagery and DEM.
- Source :
-
IEEE Transactions on Geoscience & Remote Sensing . Feb2014, Vol. 52 Issue 2, p1336-1349. 14p. - Publication Year :
- 2014
-
Abstract
- Rainfall-induced landslides are a major threat in Taiwan, particularly during the typhoon season. A precise survey of landslides after a super event is a critical task for disaster, watershed, and forestry land management. In this paper, we utilize high spatial resolution multispectral optical imagery and a digital elevation model (DEM) with an object-oriented analysis technique to develop a scheme for the recognition of landslides using multilevel segmentation and a hierarchical semantic network. Four case studies are presented to evaluate the feasibility of the proposed scheme. Three kinds of remote sensing imagery, namely pan-sharpened FORMOSAT-2 satellite images, aerial digital images from Z/I digital mapping camera, and images acquired by a digital single lens reflex camera mounted on a fixed-wing unmanned aerial vehicle are used. An accuracy assessment is accomplished by evaluating three test sites containing hundreds of landslides associated with the Typhoon Morakot. The input data include ortho-rectified image and DEM. Four spectral and one topographic object features are derived for semiautomatic landslide recognition. The threshold values are determined semiautomatically by statistical estimation from a few training samples. The experimental results show that the proposed approach can counteract the commission/omission errors and achieve missing/branching factors at less than 0.12 with a quality percentage of 81.7%. The results demonstrate the feasibility and accuracy of the proposed landslide recognition scheme even when different optical sensors are utilized. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 52
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 101186527
- Full Text :
- https://doi.org/10.1109/TGRS.2013.2250293