Next Article in Journal
Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR
Next Article in Special Issue
Multi-Objective Emergency Material Vehicle Dispatching and Routing under Dynamic Constraints in an Earthquake Disaster Environment
Previous Article in Journal
Modeling the Hourly Distribution of Population at a High Spatiotemporal Resolution Using Subway Smart Card Data: A Case Study in the Central Area of Beijing
Previous Article in Special Issue
A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(5), 131; doi:10.3390/ijgi6050131

Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
College of Electronic and Information, Yangtze University, Jingzhou 434023, China
3
Military Region of Hubei Province, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Milan Konecny and Wolfgang Kainz
Received: 28 January 2017 / Revised: 20 April 2017 / Accepted: 25 April 2017 / Published: 27 April 2017
View Full-Text   |   Download PDF [8620 KB, uploaded 5 May 2017]   |  

Abstract

The detection of damaged building regions is crucial to emergency response actions and rescue work after a disaster. Change detection methods using multi-temporal remote sensing images are widely used for this purpose. Differing from traditional methods based on change detection for damaged building regions, semantic scene change can provide a new point of view since it can indicate the land-use variation at the semantic level. In this paper, a novel method is proposed for detecting damaged building regions based on semantic scene change in a visual Bag-of-Words model. Pre- and post-disaster scene change in building regions are represented by a uniform visual codebook frequency. The scene change of damaged and non-damaged building regions is discriminated using the Support Vector Machine (SVM) classifier. An evaluation of experimental results, for a selected study site of the Longtou hill town of Yunnan, China, which was heavily damaged in the Ludian earthquake of 14 March 2013, shows that this method is feasible and effective for detecting damaged building regions. For the experiments, WorldView-2 optical imagery and aerial imagery is used. View Full-Text
Keywords: detection of damaged building region; scene classification; scene change; visual bag of words; SVM detection of damaged building region; scene classification; scene change; visual bag of words; SVM
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Tu, J.; Li, D.; Feng, W.; Han, Q.; Sui, H. Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images. ISPRS Int. J. Geo-Inf. 2017, 6, 131.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top