MATLAB code to estimate landslide volume from single remote sensed image using genetic algorithm and imagery similarity measurement

Ting Shiuan Wang, Teng To Yu, Shing Tsz Lee, Wen Fei Peng, Wei Ling Lin, Pei Ling Li

研究成果: Article

7 引文 (Scopus)

摘要

Information regarding the scale of a hazard is crucial for the evaluation of its associated impact. Quantitative analysis of landslide volume immediately following the event can offer better understanding and control of contributory factors and their relative importance. Such information cannot be gathered for each landslide event, owing to limitations in obtaining useable raw data and the necessary procedures of each applied technology. Empirical rules are often used to predict volume change, but the resulting accuracy is very low. Traditional methods use photogrammetry or light detection and ranging (LiDAR) to produce a post-event digital terrain model (DTM). These methods are both costly and time-intensive. This study presents a technique to estimate terrain change volumes quickly and easily, not only reducing waiting time but also offering results with less than 25% error. A genetic algorithm (GA) programmed MATLAB is used to intelligently predict the elevation change for each pixel of an image. This deviation from the pre-event DTM becomes a candidate for the post-event DTM. Thus, each changed DTM is converted into a shadow relief image and compared with a single post-event remotely sensed image for similarity ranking. The candidates ranked in the top two thirds are retained as parent chromosomes to produce offspring in the next generation according to the rules of GAs. When the highest similarity index reaches 0.75, the DTM corresponding to that hillshade image is taken as the calculated post-event DTM. As an example, a pit with known volume is removed from a flat, inclined plane to demonstrate the theoretical capability of the code. The method is able to rapidly estimate the volume of terrain change within an error of 25%, without the delays involved in obtaining stereo image pairs, or the need for ground control points (GCPs) or professional photogrammetry software.

原文English
頁(從 - 到)238-247
頁數10
期刊Computers and Geosciences
70
DOIs
出版狀態Published - 2014 一月 1

指紋

digital terrain model
Landslides
genetic algorithm
MATLAB
landslide
imagery
Genetic algorithms
Photogrammetry
volume change
photogrammetry
stereo image
similarity index
ground control
Chromosomes
quantitative analysis
ranking
code
chromosome
Hazards
pixel

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computers in Earth Sciences

引用此文

@article{db5f3e8f88c84f8ba49fc2f563da3465,
title = "MATLAB code to estimate landslide volume from single remote sensed image using genetic algorithm and imagery similarity measurement",
abstract = "Information regarding the scale of a hazard is crucial for the evaluation of its associated impact. Quantitative analysis of landslide volume immediately following the event can offer better understanding and control of contributory factors and their relative importance. Such information cannot be gathered for each landslide event, owing to limitations in obtaining useable raw data and the necessary procedures of each applied technology. Empirical rules are often used to predict volume change, but the resulting accuracy is very low. Traditional methods use photogrammetry or light detection and ranging (LiDAR) to produce a post-event digital terrain model (DTM). These methods are both costly and time-intensive. This study presents a technique to estimate terrain change volumes quickly and easily, not only reducing waiting time but also offering results with less than 25{\%} error. A genetic algorithm (GA) programmed MATLAB is used to intelligently predict the elevation change for each pixel of an image. This deviation from the pre-event DTM becomes a candidate for the post-event DTM. Thus, each changed DTM is converted into a shadow relief image and compared with a single post-event remotely sensed image for similarity ranking. The candidates ranked in the top two thirds are retained as parent chromosomes to produce offspring in the next generation according to the rules of GAs. When the highest similarity index reaches 0.75, the DTM corresponding to that hillshade image is taken as the calculated post-event DTM. As an example, a pit with known volume is removed from a flat, inclined plane to demonstrate the theoretical capability of the code. The method is able to rapidly estimate the volume of terrain change within an error of 25{\%}, without the delays involved in obtaining stereo image pairs, or the need for ground control points (GCPs) or professional photogrammetry software.",
author = "Wang, {Ting Shiuan} and Yu, {Teng To} and Lee, {Shing Tsz} and Peng, {Wen Fei} and Lin, {Wei Ling} and Li, {Pei Ling}",
year = "2014",
month = "1",
day = "1",
doi = "10.1016/j.cageo.2014.06.004",
language = "English",
volume = "70",
pages = "238--247",
journal = "Computers and Geosciences",
issn = "0098-3004",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - MATLAB code to estimate landslide volume from single remote sensed image using genetic algorithm and imagery similarity measurement

AU - Wang, Ting Shiuan

AU - Yu, Teng To

AU - Lee, Shing Tsz

AU - Peng, Wen Fei

AU - Lin, Wei Ling

AU - Li, Pei Ling

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Information regarding the scale of a hazard is crucial for the evaluation of its associated impact. Quantitative analysis of landslide volume immediately following the event can offer better understanding and control of contributory factors and their relative importance. Such information cannot be gathered for each landslide event, owing to limitations in obtaining useable raw data and the necessary procedures of each applied technology. Empirical rules are often used to predict volume change, but the resulting accuracy is very low. Traditional methods use photogrammetry or light detection and ranging (LiDAR) to produce a post-event digital terrain model (DTM). These methods are both costly and time-intensive. This study presents a technique to estimate terrain change volumes quickly and easily, not only reducing waiting time but also offering results with less than 25% error. A genetic algorithm (GA) programmed MATLAB is used to intelligently predict the elevation change for each pixel of an image. This deviation from the pre-event DTM becomes a candidate for the post-event DTM. Thus, each changed DTM is converted into a shadow relief image and compared with a single post-event remotely sensed image for similarity ranking. The candidates ranked in the top two thirds are retained as parent chromosomes to produce offspring in the next generation according to the rules of GAs. When the highest similarity index reaches 0.75, the DTM corresponding to that hillshade image is taken as the calculated post-event DTM. As an example, a pit with known volume is removed from a flat, inclined plane to demonstrate the theoretical capability of the code. The method is able to rapidly estimate the volume of terrain change within an error of 25%, without the delays involved in obtaining stereo image pairs, or the need for ground control points (GCPs) or professional photogrammetry software.

AB - Information regarding the scale of a hazard is crucial for the evaluation of its associated impact. Quantitative analysis of landslide volume immediately following the event can offer better understanding and control of contributory factors and their relative importance. Such information cannot be gathered for each landslide event, owing to limitations in obtaining useable raw data and the necessary procedures of each applied technology. Empirical rules are often used to predict volume change, but the resulting accuracy is very low. Traditional methods use photogrammetry or light detection and ranging (LiDAR) to produce a post-event digital terrain model (DTM). These methods are both costly and time-intensive. This study presents a technique to estimate terrain change volumes quickly and easily, not only reducing waiting time but also offering results with less than 25% error. A genetic algorithm (GA) programmed MATLAB is used to intelligently predict the elevation change for each pixel of an image. This deviation from the pre-event DTM becomes a candidate for the post-event DTM. Thus, each changed DTM is converted into a shadow relief image and compared with a single post-event remotely sensed image for similarity ranking. The candidates ranked in the top two thirds are retained as parent chromosomes to produce offspring in the next generation according to the rules of GAs. When the highest similarity index reaches 0.75, the DTM corresponding to that hillshade image is taken as the calculated post-event DTM. As an example, a pit with known volume is removed from a flat, inclined plane to demonstrate the theoretical capability of the code. The method is able to rapidly estimate the volume of terrain change within an error of 25%, without the delays involved in obtaining stereo image pairs, or the need for ground control points (GCPs) or professional photogrammetry software.

UR - http://www.scopus.com/inward/record.url?scp=84903908712&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84903908712&partnerID=8YFLogxK

U2 - 10.1016/j.cageo.2014.06.004

DO - 10.1016/j.cageo.2014.06.004

M3 - Article

AN - SCOPUS:84903908712

VL - 70

SP - 238

EP - 247

JO - Computers and Geosciences

JF - Computers and Geosciences

SN - 0098-3004

ER -