Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery

Johansen, Kasper, Arroyo, Lara A., Phinn, Stuart and Witte, Christian (2010). Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery. In: Russell G. Congalton, Special Issue on Geographic Object-Based Image Analysis (GEOBIA). GEOBIA 2008: Pixels, Objects, Intelligence. GEOgraphic Object Based Image Analysis for the 21st Century, Calgary, Alberta, Canada, (123-136). 5-8 August 2008.

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
Author Johansen, Kasper
Arroyo, Lara A.
Phinn, Stuart
Witte, Christian
Title of paper Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery
Conference name GEOBIA 2008: Pixels, Objects, Intelligence. GEOgraphic Object Based Image Analysis for the 21st Century
Conference location Calgary, Alberta, Canada
Conference dates 5-8 August 2008
Proceedings title Special Issue on Geographic Object-Based Image Analysis (GEOBIA)   Check publisher's open access policy
Journal name Photogrammetric Engineering and Remote Sensing   Check publisher's open access policy
Place of Publication Bethesda, MD, U.S.A.
Publisher American Society for Photogrammetry and Remote Sensing
Publication Year 2010
Sub-type Fully published paper
ISSN 0099-1112
Editor Russell G. Congalton
Volume 76
Issue 2
Start page 123
End page 136
Total pages 14
Collection year 2011
Language eng
Formatted Abstract/Summary
The objectives of this research were to (a) develop a geo-object-based classification system for accurately mapping riparian land-cover classes for two QuickBird images, and (b) compare change maps derived from geo-object-based and per-pixel inputs used in three change detection techniques. The change detection techniques included post-classification comparison, image differencing, and the tasseled cap transformation. Two QuickBird images, atmospherically corrected to at-surface reflectance, were captured in May and August 2007 for a savanna woodlands area along Mimosa Creek in Central Queensland, Australia. Concurrent in-situ land-cover identification and lidar data were used for calibration and validation. The geo-object-based classification results showed that the use of class-related features and membership functions could be standardized for classifying the two QuickBird images. The geo-object-based inputs provided more accurate change detection results than those derived from the pixel-based inputs, as the geo-object-based approach reduced mis-registration and shadowing effects and allowed inclusion of context relationships.
© 2010 American Society for Photogrammetry and Remote Sensing
Subjects 0909 Geomatic Engineering
Keyword Remote-sensing data
Multiscale
Change detection
High spatial resolution
Object-Oriented change detection
Definiens developer
Rule sets
Riparian zones
QuickBird
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published February 2010.

 
Available Versions of this Record
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 36 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Sun, 07 Mar 2010, 00:01:30 EST