Development of process trees for object-oriented change detection in riparian environments from high spatial resolution multi-spectral images

Johansen, Kasper, Arroyo, L. A. and Phinn, Stuart R. (2008). Development of process trees for object-oriented change detection in riparian environments from high spatial resolution multi-spectral images. In: Geoffrey J. Hay, Thomas Blaschke and Danielle Marceau, Proceedings of GEOBIA: GEOBIA 2008 - Pixels, Objects, Intelligence. GEOgraphic Object Based Image Analysis for the 21st Century. GEOBIA 2008 - Pixels, Objects, Intelligence. GEOgraphic Object Based Image Analysis for the 21st Century, Calgary, Canada, (1-6). August 2008.

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Author Johansen, Kasper
Arroyo, L. A.
Phinn, Stuart R.
Title of paper Development of process trees for object-oriented change detection in riparian environments from high spatial resolution multi-spectral images
Conference name GEOBIA 2008 - Pixels, Objects, Intelligence. GEOgraphic Object Based Image Analysis for the 21st Century
Conference location Calgary, Canada
Conference dates August 2008
Proceedings title Proceedings of GEOBIA: GEOBIA 2008 - Pixels, Objects, Intelligence. GEOgraphic Object Based Image Analysis for the 21st Century
Place of Publication Calgary, Alberta, Canada
Publisher University of Calgary
Publication Year 2008
Sub-type Fully published paper
Open Access Status File (Publisher version)
ISSN 1682-1777
Editor Geoffrey J. Hay
Thomas Blaschke
Danielle Marceau
Volume 38 (XXXVIII)
Issue 4 / C1
Start page 1
End page 6
Total pages 6
Collection year 2009
Language eng
Abstract/Summary The objectives of this research were to: (1) develop rule sets in Definiens Developer 7® for mapping and monitoring riparian zone land-cover classes within two QuickBird images; and (2) compare the results of four object-oriented and pixel-based change detection approaches. 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. In-situ vegetation structural measurements and LiDAR data, obtained on 28 May - 5 June and 15 July 2007 respectively, were used for calibration and validation. A sequential segmentation routine was applied to enable segmentation of large image datasets. An Isodata unsupervised classification was used for pixel-based classification and rule sets were developed for object-oriented classification of the following land-cover classes: streambed; riparian vegetation; bare ground; rangelands; and woodlands. Four object-oriented and pixel-based change detection routines were applied to the image data: post-classification comparison; image differencing; image regression; and the tasselled cap transformation. The object-oriented classification results showed that object- and class-related features and membership functions could be standardized in the rule sets for classifying the two QuickBird images. Results from the different change detection approaches indicated that post-classification comparison and image differencing produced more accurate results, especially when used together. All four change detection approaches were suited to object-oriented analysis. Advantages of the object-oriented change detection routines included: (1) no need for post-change detection filtering and smoothing; (2) less impact of slight geometric offsets between image datasets; and (3) the ability to include context relationships to improve change detection results.
Subjects E1
090905 Photogrammetry and Remote Sensing
96 Environment
Keyword Object-Oriented Change Detection
Definiens Developer
Rule Sets
Riparian Zones
Quic
Q-Index Code E1
Q-Index Status Confirmed Code
Additional Notes Title on attached PDF -- Object-oriented change detection of riparian environments from high spatial resolution multi-spectral images.

 
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Created: Wed, 15 Apr 2009, 13:35:52 EST by Helen Smith on behalf of School of Geography, Planning & Env Management