TY - GEN
T1 - L1-graph semisupervised learning for hyperspectral image classification
AU - Gu, Yanfeng
AU - Feng, Kai
PY - 2012
Y1 - 2012
N2 - Recently, research in semisupervised learning (SSL) based on sparse representation has shown huge potential for many classification tasks. In this paper, we address a hyperspectral image classification by integrating L1-graph and SSL. We propose a semisupervised classification method with L1-graph which has more attractive merits than traditional graph method, such as parameter free, sparsity and robustness. Our method firstly obtains the graph weights by solving a L1 optimization problem, and then generates a way of SSL with the L1-graph weights to deal with classification of hyperspectral images. The experiments are designed to cope with challenging real hyperspectral image classification task with a few labeled samples. The experimental results demonstrate the effectiveness of the L1-graph semisupervised method.
AB - Recently, research in semisupervised learning (SSL) based on sparse representation has shown huge potential for many classification tasks. In this paper, we address a hyperspectral image classification by integrating L1-graph and SSL. We propose a semisupervised classification method with L1-graph which has more attractive merits than traditional graph method, such as parameter free, sparsity and robustness. Our method firstly obtains the graph weights by solving a L1 optimization problem, and then generates a way of SSL with the L1-graph weights to deal with classification of hyperspectral images. The experiments are designed to cope with challenging real hyperspectral image classification task with a few labeled samples. The experimental results demonstrate the effectiveness of the L1-graph semisupervised method.
KW - hyperspectral image classification
KW - L1 graph
KW - semisupervised learning
KW - sparse representation
UR - https://www.scopus.com/pages/publications/84873196937
U2 - 10.1109/IGARSS.2012.6351274
DO - 10.1109/IGARSS.2012.6351274
M3 - 会议稿件
AN - SCOPUS:84873196937
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1401
EP - 1404
BT - IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
Y2 - 22 July 2012 through 27 July 2012
ER -