TY - GEN
T1 - L2,0-norm regularization based feature selection for very high resolution remote sensing images
AU - Chen, Xi
AU - Gu, Yanfeng
AU - Zhang, Ye
AU - Yan, Yiming
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/10
Y1 - 2015/11/10
N2 - This paper presents a ℓ2,0-norm regularization based feature selection method to analyze very high resolution remote sensing imagery. The method tackles the feature selection problem based on a ℓ2,1-norm based objective function and a ℓ2, 0-norm equality constraint. The constrained optimization problem is solved by an efficient algorithm based on augmented Lagrangian method to figure out a stable local solution. Though the ℓ2, 0-norm regularization based feature selection method should handle a non-convex and non-smooth problem, it outperforms the ℓ2,1-norm regularization based approximate convex counterparts and state-of-art feature selection methods in light of classification accuracies by 1-NN and SVM classifiers. The experimental results demonstrate the effectiveness of the presented method in selecting features with great generalization capabilities.
AB - This paper presents a ℓ2,0-norm regularization based feature selection method to analyze very high resolution remote sensing imagery. The method tackles the feature selection problem based on a ℓ2,1-norm based objective function and a ℓ2, 0-norm equality constraint. The constrained optimization problem is solved by an efficient algorithm based on augmented Lagrangian method to figure out a stable local solution. Though the ℓ2, 0-norm regularization based feature selection method should handle a non-convex and non-smooth problem, it outperforms the ℓ2,1-norm regularization based approximate convex counterparts and state-of-art feature selection methods in light of classification accuracies by 1-NN and SVM classifiers. The experimental results demonstrate the effectiveness of the presented method in selecting features with great generalization capabilities.
KW - Object-oriented Image Analysis
KW - Sparse Regularization
KW - Supervised Feature Selection
KW - ℓ-norm
UR - https://www.scopus.com/pages/publications/84962495395
U2 - 10.1109/IGARSS.2015.7325808
DO - 10.1109/IGARSS.2015.7325808
M3 - 会议稿件
AN - SCOPUS:84962495395
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 493
EP - 496
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 -