DocumentCode
547363
Title
Study on remote sensing image classification based on artificial immune system
Author
Qin, Xiaoqian
Author_Institution
Sch. of Urban & Environ. Sci., Huaiyin Normal Univ., Huai-an, China
Volume
3
fYear
2011
fDate
10-12 June 2011
Firstpage
529
Lastpage
533
Abstract
On the base of analyzing the disadvantages of the negative selection algorithm, a bidirectional selection algorithm based on artificial immune system is presented. Clone selection and mutation algorithms are used on the training sample set to obtain mature antibody set. Then the mature antibody set can be used to classify the remote sensing image. It is demonstrated that this algorithm is superior to conventional maximum likelihood classification. Its accuracy reaches 87.4 percent.
Keywords
artificial immune systems; biology computing; image classification; maximum likelihood estimation; remote sensing; artificial immune system; bidirectional selection algorithm; clone selection; mature antibody set; maximum likelihood classification; mutation algorithms; negative selection algorithm; remote sensing image classification; artificial immune system; image classification; negative selection; pattern recognition; remote sensing;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4244-8727-1
Type
conf
DOI
10.1109/CSAE.2011.5952734
Filename
5952734
Link To Document