DocumentCode :
2087578
Title :
Active Learning with Support Vector Machines in Remotely Sensed Image Classification
Author :
Sun, Zhichao ; Liu, Zhigang ; Liu, Suhong ; Zhang, Yun ; Yang, Bing
Author_Institution :
State Key Lab. of Remote Sensing Sci., Beijing Normal Univ., Beijing, China
fYear :
2009
fDate :
17-19 Oct. 2009
Firstpage :
1
Lastpage :
6
Abstract :
Support vector machine (SVM) has been widely applied in the classification of remotely sensed image. How to reduce support vector number in SVM classifier so as to reduce classification time still an important open problem, especially in the case of mass data. To obtain fast classifier with high accuracy, an active learning schema is proposed in the SVM based image classification. Experimental results with synthetic data and multispectral remotely sensed images show that, compare with the SVM classifiers trained with whole training sample set in a time, the SVM classifiers obtained by active selection of training instances have much fewer support vector and can always achieve relatively higher accuracy.
Keywords :
image classification; support vector machines; SVM classifier; active learning; remotely sensed image classification; support vector machines; Cities and towns; Data mining; Geography; Image classification; Machine learning; Quadratic programming; Remote sensing; Support vector machine classification; Support vector machines; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4129-7
Electronic_ISBN :
978-1-4244-4131-0
Type :
conf
DOI :
10.1109/CISP.2009.5301576
Filename :
5301576
Link To Document :
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