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 |
| 卷 | 70 |
| DOIs | |
| 出版狀態 | Published - 2014 一月 1 |
指紋
All Science Journal Classification (ASJC) codes
- Information Systems
- Computers in Earth Sciences
引用此文
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MATLAB code to estimate landslide volume from single remote sensed image using genetic algorithm and imagery similarity measurement. / Wang, Ting Shiuan; Yu, Teng To; Lee, Shing Tsz; Peng, Wen Fei; Lin, Wei Ling; Li, Pei Ling.
於: Computers and Geosciences, 卷 70, 01.01.2014, p. 238-247.研究成果: Article
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.
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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 -