Dynamic learning of SCRF for feature selection and classification of hyperspectral imagery

Authors:
Ping Zhong;Zhiming Qian;Runsheng Wang
Affiliations:
ATR National Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China;ATR National Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China;ATR National Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

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Abstract

This paper investigates the feature selection and contextual classification of hyperspectral images through the sparse conditional random field (SCRF) model. To relieve the heavy degeneration of classification performance caused by the characteristics of the hyperspectral data and the oversparsity when SCRF selects a small feature subset, we develop a dynamic learning framework to train the SCRF. Under the piecewise training framework, the proposed dynamic learning method of SCRF can be implemented efficiently through separated dynamic sparse trainings of simple classifiers defined by corresponding potentials. Experiments on the real-world hyperspectral images attest to the effectiveness of the proposed method.