Next Article in Journal / Special Issue
An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology
Previous Article in Journal
Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and Boosted Regression Trees
Previous Article in Special Issue
Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(4), 326; doi:10.3390/rs9040326

Stratified Template Matching to Support Refugee Camp Analysis in OBIA Workflows

Department of Geoinformatics (Z_GIS), University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Norman Kerle, Markus Gerke, Sébastien Lefèvre, Xiaofeng Li and Prasad S. Thenkabail
Received: 30 December 2016 / Revised: 22 March 2017 / Accepted: 27 March 2017 / Published: 30 March 2017
View Full-Text   |   Download PDF [9642 KB, uploaded 30 March 2017]   |  

Abstract

Accurate and reliable information about the situation in refugee or internally displaced person camps is very important for planning any kind of help like health care, infrastructure, or vaccination campaigns. The number and spatial distribution of single dwellings extracted semi-automatically from very high-resolution (VHR) satellite imagery as an indicator for population estimations can provide such important information. The accuracy of the extracted dwellings can vary quite a lot depending on various factors. To enhance established single dwelling extraction approaches, we have tested the integration of stratified template matching methods in object-based image analysis (OBIA) workflows. A template library for various dwelling types (template samples are taken from ten different sites using 16 satellite images), incorporating the shadow effect of dwellings, was established. Altogether, 18 template classes were created covering typically occurring dwellings and their cast shadows. The created template library aims to be generally applicable in similar conditions. Compared to pre-existing OBIA classifications, the approach could increase the producer’s accuracy by 11.7 percentage points on average and slightly increase the user’s accuracy. These results show that the stratified integration of template matching approaches in OBIA workflows is a possibility to further improve the results of semi-automated dwelling extraction, especially in complex situations. View Full-Text
Keywords: object-based image analysis (OBIA); template matching; object detection; dwelling library; refugee and IDP camps; VHR data object-based image analysis (OBIA); template matching; object detection; dwelling library; refugee and IDP camps; VHR data
Figures

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

Tiede, D.; Krafft, P.; Füreder, P.; Lang, S. Stratified Template Matching to Support Refugee Camp Analysis in OBIA Workflows. Remote Sens. 2017, 9, 326.

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]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top