Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method

Zhongbin Li, Wenzhong Shi, Soe Myint, Ping Lu, Qunming Wang

Research output: Contribution to journalArticle

42 Citations (Scopus)

Abstract

Landslide inventory mapping (LIM) is an increasingly important research topic in remote sensing and natural hazards. Past studies achieve LIM mainly using on-screen interpretation of aerial photos, and little attention has been paid to developing more automated methods. In recent years, the use of multitemporal remote sensing images makes it possible to map landslides semi-automatically. Although numerous methods have been proposed, only a few methods are competent for some specific situations and there is large room for improvement in their degree of automation. For these reasons, a semi-automated approach is proposed for reliable and accurate LIM from bitemporal aerial orthophotos. Specifically, it consists of two principal steps: 1) change detection-based thresholding (CDT) and 2) level set evolution (LSE). CDT is mainly used to generate the initial zero-level curve (ZLC) for LSE, thus automating the proposed method considerably. It includes three substeps: 1) generating difference image (DI) using change vector analysis (CVA), 2) detecting landslide candidates using a thresholding method, and 3) removing errors using morphology operations. Then, landslide boundaries are detected using two types of LSE, i.e., edge-based LSE (ELSE) and region-based LSE (RLSE). Finally, the effectiveness and advantages of the proposed methods are corroborated by a series of experiments. Given its efficiency and accuracy, it can be applied to rapid responses of natural hazards. This study is the first attempt to apply LSE to LIM from bitemporal remote sensing images.

Original languageEnglish (US)
Pages (from-to)215-230
Number of pages16
JournalRemote Sensing of Environment
Volume175
DOIs
StatePublished - Mar 15 2016

Fingerprint

landslides
Landslides
aerial photograph
photographs
landslide
Antennas
remote sensing
Remote sensing
natural hazard
methodology
Hazards
orthophoto
aerial photography
automation
method
detection
Automation

Keywords

  • Aerial orthophoto
  • Change detection
  • Change vector analysis (CVA)
  • Landslide inventory mapping (LIM)
  • Level set evolution (LSE)

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Soil Science
  • Geology

Cite this

Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. / Li, Zhongbin; Shi, Wenzhong; Myint, Soe; Lu, Ping; Wang, Qunming.

In: Remote Sensing of Environment, Vol. 175, 15.03.2016, p. 215-230.

Research output: Contribution to journalArticle

@article{cadcfe6a74cd4c2f8d59fa9273d16b68,
title = "Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method",
abstract = "Landslide inventory mapping (LIM) is an increasingly important research topic in remote sensing and natural hazards. Past studies achieve LIM mainly using on-screen interpretation of aerial photos, and little attention has been paid to developing more automated methods. In recent years, the use of multitemporal remote sensing images makes it possible to map landslides semi-automatically. Although numerous methods have been proposed, only a few methods are competent for some specific situations and there is large room for improvement in their degree of automation. For these reasons, a semi-automated approach is proposed for reliable and accurate LIM from bitemporal aerial orthophotos. Specifically, it consists of two principal steps: 1) change detection-based thresholding (CDT) and 2) level set evolution (LSE). CDT is mainly used to generate the initial zero-level curve (ZLC) for LSE, thus automating the proposed method considerably. It includes three substeps: 1) generating difference image (DI) using change vector analysis (CVA), 2) detecting landslide candidates using a thresholding method, and 3) removing errors using morphology operations. Then, landslide boundaries are detected using two types of LSE, i.e., edge-based LSE (ELSE) and region-based LSE (RLSE). Finally, the effectiveness and advantages of the proposed methods are corroborated by a series of experiments. Given its efficiency and accuracy, it can be applied to rapid responses of natural hazards. This study is the first attempt to apply LSE to LIM from bitemporal remote sensing images.",
keywords = "Aerial orthophoto, Change detection, Change vector analysis (CVA), Landslide inventory mapping (LIM), Level set evolution (LSE)",
author = "Zhongbin Li and Wenzhong Shi and Soe Myint and Ping Lu and Qunming Wang",
year = "2016",
month = "3",
day = "15",
doi = "10.1016/j.rse.2016.01.003",
language = "English (US)",
volume = "175",
pages = "215--230",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method

AU - Li, Zhongbin

AU - Shi, Wenzhong

AU - Myint, Soe

AU - Lu, Ping

AU - Wang, Qunming

PY - 2016/3/15

Y1 - 2016/3/15

N2 - Landslide inventory mapping (LIM) is an increasingly important research topic in remote sensing and natural hazards. Past studies achieve LIM mainly using on-screen interpretation of aerial photos, and little attention has been paid to developing more automated methods. In recent years, the use of multitemporal remote sensing images makes it possible to map landslides semi-automatically. Although numerous methods have been proposed, only a few methods are competent for some specific situations and there is large room for improvement in their degree of automation. For these reasons, a semi-automated approach is proposed for reliable and accurate LIM from bitemporal aerial orthophotos. Specifically, it consists of two principal steps: 1) change detection-based thresholding (CDT) and 2) level set evolution (LSE). CDT is mainly used to generate the initial zero-level curve (ZLC) for LSE, thus automating the proposed method considerably. It includes three substeps: 1) generating difference image (DI) using change vector analysis (CVA), 2) detecting landslide candidates using a thresholding method, and 3) removing errors using morphology operations. Then, landslide boundaries are detected using two types of LSE, i.e., edge-based LSE (ELSE) and region-based LSE (RLSE). Finally, the effectiveness and advantages of the proposed methods are corroborated by a series of experiments. Given its efficiency and accuracy, it can be applied to rapid responses of natural hazards. This study is the first attempt to apply LSE to LIM from bitemporal remote sensing images.

AB - Landslide inventory mapping (LIM) is an increasingly important research topic in remote sensing and natural hazards. Past studies achieve LIM mainly using on-screen interpretation of aerial photos, and little attention has been paid to developing more automated methods. In recent years, the use of multitemporal remote sensing images makes it possible to map landslides semi-automatically. Although numerous methods have been proposed, only a few methods are competent for some specific situations and there is large room for improvement in their degree of automation. For these reasons, a semi-automated approach is proposed for reliable and accurate LIM from bitemporal aerial orthophotos. Specifically, it consists of two principal steps: 1) change detection-based thresholding (CDT) and 2) level set evolution (LSE). CDT is mainly used to generate the initial zero-level curve (ZLC) for LSE, thus automating the proposed method considerably. It includes three substeps: 1) generating difference image (DI) using change vector analysis (CVA), 2) detecting landslide candidates using a thresholding method, and 3) removing errors using morphology operations. Then, landslide boundaries are detected using two types of LSE, i.e., edge-based LSE (ELSE) and region-based LSE (RLSE). Finally, the effectiveness and advantages of the proposed methods are corroborated by a series of experiments. Given its efficiency and accuracy, it can be applied to rapid responses of natural hazards. This study is the first attempt to apply LSE to LIM from bitemporal remote sensing images.

KW - Aerial orthophoto

KW - Change detection

KW - Change vector analysis (CVA)

KW - Landslide inventory mapping (LIM)

KW - Level set evolution (LSE)

UR - http://www.scopus.com/inward/record.url?scp=84954175770&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84954175770&partnerID=8YFLogxK

U2 - 10.1016/j.rse.2016.01.003

DO - 10.1016/j.rse.2016.01.003

M3 - Article

AN - SCOPUS:84954175770

VL - 175

SP - 215

EP - 230

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -