TY - GEN
T1 - Gabor feature based dictionary fusion for hyperspectral imagery classification
AU - Jia, Sen
AU - Hu, Jie
AU - Tang, Guihua
AU - Shen, Linlin
AU - Deng, Lin
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/10
Y1 - 2015/11/10
N2 - Multiple kinds of features extracted from hyperspectral imagery (HSI) have shown great potential for pixel-oriented classification. However, two difficulties can be encountered during the classification process. Firstly, it is time consuming to directly utilize the large amount of features. Secondly, because each kind of feature is usually processed individually, the high-level relationship among different features is not completely configured, decreasing the performance eventually. In this paper, a new strategy to fuse the features and exploit dictionary learning for HSI classification is proposed. Based on the high-level relationship, the extracted Gabor features have been integrated into a more compact and more discriminative representation through a Fisher-based criterion. Experimental results have shown that the fused features can not only produce competitive performance for HSI classification, but also greatly reduce the computational complexity.
AB - Multiple kinds of features extracted from hyperspectral imagery (HSI) have shown great potential for pixel-oriented classification. However, two difficulties can be encountered during the classification process. Firstly, it is time consuming to directly utilize the large amount of features. Secondly, because each kind of feature is usually processed individually, the high-level relationship among different features is not completely configured, decreasing the performance eventually. In this paper, a new strategy to fuse the features and exploit dictionary learning for HSI classification is proposed. Based on the high-level relationship, the extracted Gabor features have been integrated into a more compact and more discriminative representation through a Fisher-based criterion. Experimental results have shown that the fused features can not only produce competitive performance for HSI classification, but also greatly reduce the computational complexity.
KW - HSI classification
KW - dictionary fusion
KW - sparse coding
UR - http://www.scopus.com/inward/record.url?scp=84962602232&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2015.7325793
DO - 10.1109/IGARSS.2015.7325793
M3 - Conference contribution
AN - SCOPUS:84962602232
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 433
EP - 436
BT - 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Y2 - 26 July 2015 through 31 July 2015
ER -