Paper Info
Title
Building damage detection from post-quake remote sensing image based on fuzzy reasoning
Abstract
The paper presents an approach for building damage detection from high resolution remote sensing image using multi-feature analysis and the fuzzy reasoning procedure. The selected area of our study is in Yushu, which was strongly hit by 7.1-magnitude earthquake. The study area contains 101 buildings, of which 46 are collapsed and 55 are un-collapsed. First, the buildings were selected one-by-one from the GIS data and remote sensing image. Second, three categories of features were analyzed to describe the differences between the collapsed buildings and un-collapsed ones, including spectral feature, texture feature and gradient feature. Last, a final decision was made through considering the variety of feature parameters utilizing fuzzy reasoning. The overall accuracy of building damage detection was 91.09%, of the total 46 collapsed buildings, 42 were detected correctly by the proposed approach, giving 91.30% producer's accuracy.
Year
DOI
Venue
2014
10.1109/IGARSS.2014.6946476
IGARSS
Keywords
Field
DocType
remote sensing,fuzzy reasoning,multifeature analysis,collapsed building,geographic information systems,fuzzy reasoning procedure,disasters,uncollapsed buildings,building damage detection,china,gis data,spectral feature,damage detection,earthquakes,fuzzy logic,feature extraction,image classification,geophysical image processing,earthquake,texture feature,post quake remote sensing,yushu,image texture,buildings (structures),gradient feature,high resolution remote sensing image,accuracy
Data mining,Computer vision,Fuzzy reasoning,Computer science,Remote sensing,Image based,Quake (series),Feature extraction,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Xin Ye1258.36
Qi-ming Qin215849.12
Mingchao Liu312.04
Jun Wang4135.63
Jianhua Wang501.01