Next Article in Journal
Analysis of Two Models for Evaluating the Energy Performance of Different Buildings
Next Article in Special Issue
A Focused Crawler for Borderlands Situation Information with Geographical Properties of Place Names
Previous Article in Journal / Special Issue
Evaluation of the Effectiveness of Border Policies in Dehong Prefecture of Yunnan, China
Article Menu

Export Article

Open AccessArticle
Sustainability 2014, 6(8), 5300-5310; doi:10.3390/su6085300

Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation

College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
*
Author to whom correspondence should be addressed.
Received: 29 May 2014 / Revised: 25 July 2014 / Accepted: 28 July 2014 / Published: 14 August 2014
(This article belongs to the Special Issue Borderland Studies and Sustainability)
View Full-Text   |   Download PDF [1443 KB, uploaded 24 February 2015]   |  

Abstract

The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of a remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Furthermore, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information) were extracted by the spectral and geometrical features. The results indicate that the edge-detection has a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% by the confusion matrix. View Full-Text
Keywords: edge-detection; object-oriented; multiresolution segmentation; spectral features; geometrical features; confusion matrix edge-detection; object-oriented; multiresolution segmentation; spectral features; geometrical features; confusion matrix
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

Shao, P.; Yang, G.; Niu, X.; Zhang, X.; Zhan, F.; Tang, T. Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation. Sustainability 2014, 6, 5300-5310.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top