Applying Object-Oriented Analysis to Segmentation and Classification of Landslide and Artificial Facilities with Remote Sensing Images
Date Issued
2010
Date
2010
Author(s)
Huang, Wei-Kai
Abstract
In this study, object-oriented analysis method was applied to interpret landslide and flood disasters before and after Typhoon Morakot with remote sensing images. It is shown that remote sensing images in wide area can be recognized quickly using this method. Two images before and after the disaster were homogeneously segmented to generate the same blocks in order to solve the boundary problem in feature classification. Based on the rules of artificial interpretation, proper information (ex. shape, spectral values, slopes , Spatial relations…etc.) was added to construct hierarchical logic, classifying 15 different kinds of features. The classification results are double checked with aerial photographs and field investigation. The overall accuracy of final training outcome based on the error matrix assessment is about 85.2%. The results show that the landslide ratio increased from 1.2% to 8.9%, blocks features of river channel increased 690 sites, area ratio increasing to 40% compared with the original river channel area, 75% of the developed lands were affected, 37% （1268 sites）of the houses were affected, 30% of the roads were affected .
Subjects
Typhoon Morakot
object-oriented
nearest neighbor method
image difference
segmentation
landslide
artificial facilities
classification
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-99-R97521117-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):8439e857d92fb28ad47ec0af2889647f