DocumentCode :
3532345
Title :
Hyperspectral remote sensing image classification based on decision level fusion
Author :
Du, Peijun ; Zhang, Wei ; Zhang, Shubi ; Xia, Junshi
Author_Institution :
China Univ. of Min. & Technol., Xuzhou, China
Volume :
4
fYear :
2009
fDate :
12-17 July 2009
Abstract :
Decision level fusion, using a specific criterion or algorithm to integrate the classified results from different classifiers, has shown great benefits to improve classification accuracy of multi-source remote sensing images. In this paper, three decision level fusion methods and four schemes for input data are used to hyperspectral remote sensing image classification. Different feature combination and decision level fusion approaches are experimented and analyzed, and the results show that decision level fusion is effective to improve the performance of hyperspectral remote sensing image classification.
Keywords :
geophysical image processing; geophysical techniques; image classification; remote sensing; classification accuracy; hyperspectral remote sensing image classification; improved D-S evidence theory; linear consensus; multi-source remote sensing images; three decision level fusion methods; Cities and towns; Classification tree analysis; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Image classification; Performance analysis; Remote sensing; Support vector machine classification; Support vector machines; decision level fusion; hyperspectral remote sensing; improved D-S evidence theory; linear consensus;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Conference_Location :
Cape Town
Print_ISBN :
978-1-4244-3394-0
Electronic_ISBN :
978-1-4244-3395-7
Type :
conf
DOI :
10.1109/IGARSS.2009.5417533
Filename :
5417533
Link To Document :
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