DocumentCode :
2001318
Title :
Vector distance algorithm for optimal segmentation scale selection of object-oriented remote sensing image classification
Author :
Yu, Huan ; Zhang, Shuqing ; Kong, Bo
Author_Institution :
Center of Remote Sensing & Geosci., CAS, Changchun, China
fYear :
2009
fDate :
12-14 Aug. 2009
Firstpage :
1
Lastpage :
6
Abstract :
Aiming at the optimal segmentation scale selection for object-oriented remote sensing image classification, thesis brought forward a new method named vector distance index. Research verified the validity and applicability of this method by carrying out experiment at wetland area with China Brazil Earth resource satellite image. Both two experiment showed that it could realize the optimal segmentation scale selection for object-oriented remote sensing image classification. Based on the basic theory of vector distance method, aiming at ldquosubmergencerdquo and ldquofragmentationrdquo phenomenon, research further brought forward a scale index, which could reflect the segmentation scale status for given object type, and provided a quantitative tool for assessing conflict degree between the two situations.
Keywords :
geographic information systems; image classification; image segmentation; China Brazil Earth resource satellite image; fragmentation phenomenon; object-oriented remote sensing image classification; optimal segmentation scale selection; submergence phenomenon; vector distance algorithm; vector distance index; wetland area; Content addressable storage; Earth; Geographic Information Systems; Geography; Geoscience and remote sensing; Hazards; Humans; Image classification; Image segmentation; Remote sensing; object-oriented classification; scale; segmentation; vector distance index; wetland;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoinformatics, 2009 17th International Conference on
Conference_Location :
Fairfax, VA
Print_ISBN :
978-1-4244-4562-2
Electronic_ISBN :
978-1-4244-4563-9
Type :
conf
DOI :
10.1109/GEOINFORMATICS.2009.5293457
Filename :
5293457
Link To Document :
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