Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and DEM

Jiann-Yeou Rau, Jyun Ping Jhan, Ruey-Juin Rau

Research output: Contribution to journalArticle

36 Citations (Scopus)

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 languageEnglish
Article number6506977
Pages (from-to)1336-1349
Number of pages14
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume52
Issue number2
DOIs
Publication statusPublished - 2014 Feb 1

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Landslides
digital elevation model
landslide
imagery
typhoon
Camera lenses
digital mapping
Fixed wings
accuracy assessment
Forestry
Optical sensors
Unmanned aerial vehicles (UAV)
digital image
Watersheds
land management
Disasters
segmentation
Rain
Remote sensing
disaster

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Cite this

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title = "Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and DEM",
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.",
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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 journalArticle

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