DocumentCode :
3021683
Title :
Marginalized kernel-based feature fusion method for VHR object classification
Author :
Chuntian Liu ; Wei Wei ; Xiao Bai ; Jun Zhou
Author_Institution :
Sch. of Comput. Sci. & Eng., Beihang Univ., Beijing, China
fYear :
2013
fDate :
21-26 July 2013
Firstpage :
216
Lastpage :
219
Abstract :
Many image features can be extracted from very high resolution remote sensing images for object classification. Proper feature combination is a step towards better classification performance. In this paper, we propose a logistic regression-based feature fusion method which assigns different weights to different features. This method considers the probability that two images belongs to the same classes and the image-to-class similarity to define the similarity between two objects. This similarity is used as a marginalized kernel for the final classifier construction. Experiments on remote sensing images suggest that this approach is effective in various feature combination, and has outperformed the SVM baseline method.
Keywords :
feature extraction; geophysical image processing; image classification; image fusion; probability; regression analysis; remote sensing; SVM baseline method; VHR object classification; image feature extraction; logistic regression-based feature fusion method; marginalized kernel-based feature fusion method; probability; remote sensing image resolution; Abstracts; Educational institutions; Support vector machines; Feature fusion; kernel method; land cover classification; remote sensing image;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
ISSN :
2153-6996
Print_ISBN :
978-1-4799-1114-1
Type :
conf
DOI :
10.1109/IGARSS.2013.6721130
Filename :
6721130
Link To Document :
بازگشت