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Comparing object-based and pixel-based classifications for mapping savannas

Whiteside, Timothy G., Boggs, Guy S. and Maier, Stefan W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation,13(6):884-893.

Document type: Journal Article

IRMA ID 82057923xPUB107
Title Comparing object-based and pixel-based classifications for mapping savannas
Author Whiteside, Timothy G.
Boggs, Guy S.
Maier, Stefan W.
Journal Name International Journal of Applied Earth Observation and Geoinformation
Publication Date 2011
Volume Number 13
Issue Number 6
ISSN 1569-8432   (check CDU catalogue open catalogue search in new window)
Scopus ID 2-s2.0-84455175710
Start Page 884
End Page 893
Total Pages 10
Place of Publication Netherlands
Publisher Elsevier BV
HERDC Category C1 - Journal Article (DIISR)
Abstract The development of robust object-based classification methods suitable for medium to high resolution satellite imagery provides a valid alternative to 'traditional' pixel-based methods. This paper compares the results of an object-based classification to a supervised per-pixel classification for mapping land cover in the tropical north of the Northern Territory of Australia. The object-based approach involved segmentation of image data into objects at multiple scale levels. Objects were assigned classes using training objects and the Nearest Neighbour supervised and fuzzy classification algorithm. The supervised pixel-based classification involved the selection of training areas and a classification using the maximum likelihood classifier algorithm. Site-specific accuracy assessment using confusion matrices of both classifications were undertaken based on 256 reference sites. A comparison of the results shows a statistically significant higher overall accuracy of the object-based classification over the pixel-based classification. The incorporation of a digital elevation model (DEM) layer and associated class rules into the objectbased classification produced slightly higher accuracies overall and for certain classes; however this was not statistically significant over the object-based using spectral information solely. The results indicate object-based analysis has good potential for extracting land cover information from satellite imagery captured over spatially heterogeneous land covers of tropical Australia.
Keywords accuracy assessment
northern Australia
object-based image analysis
tropical savanna
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Created: Fri, 29 Aug 2014, 17:05:26 CST by Anthony Hornby