Applying a Multiphase Level Set Framework For Landslide Mapping Using Remote Sensing Images

博士 === 朝陽科技大學 === 營建工程系 === 107 === Remote sensing technology has become one of the indispensable technologies for regional geological disaster investigations at home and abroad. However, satellite multispectral image interpretation of bare land classification, It is often interfered with by the mou...

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Bibliographic Details
Main Authors: CHIU, CHEN- YUAN, 邱振原
Other Authors: HSU, SUNG-CHI
Format: Others
Language:zh-TW
Published: 2019
Online Access:http://ndltd.ncl.edu.tw/handle/y3xra4
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Summary:博士 === 朝陽科技大學 === 營建工程系 === 107 === Remote sensing technology has become one of the indispensable technologies for regional geological disaster investigations at home and abroad. However, satellite multispectral image interpretation of bare land classification, It is often interfered with by the mountain's atmospheric clouds, mountain shadows and Speckle Signals, which seriously affects the correctness of the interpretation image and is prone to errors such as misjudgment or omission. This study will first study the regional SPOT 5 satellite imagery. The MATLAB was used to calculate the Normalized Difference Vegetation Index (NDVI) and the Short Wave InfraRed (SWIR). Subsequent image segmentation using the Multiphase Level Set Framework. research shows, It can effectively and quickly eliminate the problem of image shifting signals, and can divide complex terrain and features into simple objects, which can solve the problem of spatial recognition rate. Therefore, it is not affected by the resolution of the image itself, and the correctness of satellite image interpretation can be improved. In order to monitor the changes in the location of the exposed areas on the slopes along the 89th line of Renai Township in Nantou County, and the possible landslide trend. The study found that the exposed area of the mountainous areas along the 89th line from 2004 to 2013 changed the most with the single typhoon typhoon event, and the exposed slope collapsed the largest.