DocumentCode
2147358
Title
Hyperspectral data classification using classifier overproduction and fusion strategies
Author
Kuo, Bor-Chen ; Pai, Chia-Hao ; Sheu, Tian-Wei ; Chen, Guey-Shya
Author_Institution
Graduate Sch. of Educ. Meas. & Stat., Nat. Taichung Teachers Coll., Taiwan
Volume
5
fYear
2004
fDate
20-24 Sept. 2004
Firstpage
2937
Abstract
A new hybrid algorithm based on bagging and random subspace methods is proposed for improving hyperspectral data classification problem. The effects of using original data and transformed data in bagging, random subspace and the proposed algorithm are also explored. Real data experiment result shows that the proposed method performs well in both original and NWFE feature spaces.
Keywords
data acquisition; feature extraction; image classification; remote sensing; sensor fusion; NWFE feature spaces; bagging; classifier overproduction; fusion strategies; hyperspectral data classification; multiple classifier system; random subspace methods; Bagging; Boosting; Educational institutions; Extraterrestrial phenomena; Feature extraction; Hyperspectral imaging; Principal component analysis; Statistics; Training data; Voting;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International
Print_ISBN
0-7803-8742-2
Type
conf
DOI
10.1109/IGARSS.2004.1370310
Filename
1370310
Link To Document