Title :
Unsupervised Detection of Earthquake-Triggered Roof-Holes From UAV Images Using Joint Color and Shape Features
Author :
Shaodan Li ; Hong Tang ; Shi He ; Yang Shu ; Ting Mao ; Jing Li ; Zhihua Xu
Author_Institution :
State Key Lab. of Earth Surface Processes & Resource Ecology, Beijing Normal Univ., Beijing, China
Abstract :
Many methods have been developed to detect damaged buildings due to earthquake. However, little attention has been paid to analyze slightly affected buildings. In this letter, an unsupervised method is presented to detect earthquake-triggered “roof-holes” on rural houses from unmanned aerial vehicle (UAV) images. First, both orthomosaic and gradient images are generated from a set of UAV images. Then, a modified Chinese restaurant franchise model is used to learn an unsupervised model of the geo-object classes in the area by fusing both oversegmented orthomosaic and gradient images. Finally, “roof-holes” on rural houses are detected using the learned model. The performance of the proposed method is evaluated in terms of both qualitative and quantitative indexes.
Keywords :
autonomous aerial vehicles; earthquakes; geophysical techniques; UAV image; damaged building detection method; earthquake-triggered roof-hole; geo-object class; joint color feature; joint shape feature; learned model; modified Chinese restaurant franchise model; oversegmented gradient image; oversegmented orthomosaic image; qualitative index; quantitative index; rural house; unmanned aerial vehicle; unsupervised detection; unsupervised method; Buildings; Earthquakes; Feature extraction; Image color analysis; Joints; Remote sensing; Shape; Chinese restaurant franchise (CRF); image fusion; roof-hole detection; unmanned aerial vehicle (UAV) images;
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2015.2429894
