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.
| Original language | English |
|---|---|
| Article number | 6506977 |
| Pages (from-to) | 1336-1349 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Geoscience and Remote Sensing |
| Volume | 52 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2014 Feb 1 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)
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Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and DEM. / Rau, Jiann Yeou; Jhan, Jyun Ping; Rau, Ruey Juin.
In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 2, 6506977, 01.02.2014, p. 1336-1349.Research output: Contribution to journal › Article
TY - JOUR
T1 - Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and DEM
AU - Rau, Jiann Yeou
AU - Jhan, Jyun Ping
AU - Rau, Ruey Juin
PY - 2014/2/1
Y1 - 2014/2/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84890997760&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2013.2250293
DO - 10.1109/TGRS.2013.2250293
M3 - Article
AN - SCOPUS:84890997760
VL - 52
SP - 1336
EP - 1349
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
IS - 2
M1 - 6506977
ER -