TY - GEN
T1 - Efficient semi-supervised feature selection for VHR remote sensing images
AU - Chen, Xi
AU - Song, Lin
AU - Hou, Yuguan
AU - Shao, Guofan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - Feature selection is often required to select a feature subset from the original feature set of objects of very high resolution (VHR) remote sensing images. However, the majority of feature selection methods is supervised, and could fail to identify the relevant features when labeled objects are scarce. To address the problem, this paper proposes a method, efficient semi-supervised feature selection (ESFS), by effectively exploiting the underlying information of the huge amount of unlabeled objects. Firstly, probability matrix of unlabeled objects is utilized in loss function to measure the relevance of features on classes, instead of using traditional graph. Secondly, construction a l2,1-norm regularization term is imposed to ensure the sparsity in rows of the selection matrix, and consequent feature selection. Experiments are carried on a VHR image demonstrate that ESFS outperforms other classical and latest methods.
AB - Feature selection is often required to select a feature subset from the original feature set of objects of very high resolution (VHR) remote sensing images. However, the majority of feature selection methods is supervised, and could fail to identify the relevant features when labeled objects are scarce. To address the problem, this paper proposes a method, efficient semi-supervised feature selection (ESFS), by effectively exploiting the underlying information of the huge amount of unlabeled objects. Firstly, probability matrix of unlabeled objects is utilized in loss function to measure the relevance of features on classes, instead of using traditional graph. Secondly, construction a l2,1-norm regularization term is imposed to ensure the sparsity in rows of the selection matrix, and consequent feature selection. Experiments are carried on a VHR image demonstrate that ESFS outperforms other classical and latest methods.
KW - classification
KW - l-norm regularization
KW - Semi-supervised features selection
UR - https://www.scopus.com/pages/publications/85007508437
U2 - 10.1109/IGARSS.2016.7729383
DO - 10.1109/IGARSS.2016.7729383
M3 - 会议稿件
AN - SCOPUS:85007508437
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1500
EP - 1503
BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Y2 - 10 July 2016 through 15 July 2016
ER -