Typical disaster damage target extraction method based on object oriented classification

Gang Wang; Fenfei Wang; Jiansheng Chen; Shirong Chen

doi:10.1117/12.910423

15 August 2011 Typical disaster damage target extraction method based on object oriented classification

Gang Wang, Fenfei Wang, Jiansheng Chen, Shirong Chen

Proceedings Volume 8203, Remote Sensing of the Environment: The 17th China Conference on Remote Sensing; 820316 (2011) https://doi.org/10.1117/12.910423
Event: Seventeenth China Symposium on Remote Sensing, 2010, Hangzhou, China

Abstract

The extraction of typical targets (such as water, road and damaged buildings) of nature disaster is important to the emergency management, post-disaster damage assessment and disaster monitoring. This paper shows an objected oriented method to extract water, road and building targets in the earthquake of Wenchuan(2008), Yushu(2010) and Haiti(2010). We built the optimal feature sets with spectral features, shape features, texture features and context features. Experiment result shows that this method can extract flood area, road and damaged buildings effectively and achieve a relatively high accuracy. These experimental studies are leading to the opportunity to integrate classical damage survey and image oriented semi-automatic interpretation.

Citation Download Citation

Gang Wang, Fenfei Wang, Jiansheng Chen, and Shirong Chen "Typical disaster damage target extraction method based on object oriented classification", Proc. SPIE 8203, Remote Sensing of the Environment: The 17th China Conference on Remote Sensing, 820316 (15 August 2011); https://doi.org/10.1117/12.910423

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available