Affiliations: College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, China
Correspondence:
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Corresponding author. Chengming Qi, College of Urban Rail Transit and Logistics, Beijing Union University, Beijing 100101, China. E-mail: qicm@buu.edu.cn.
Abstract: Feature selection aims at selecting a feature subset that has the most discriminative information and preserve most of characteristics from original features in HyperSpectral Image (HSI) classification. This paper proposes a two-stage feature selection method based on Mutual Information (MI) and Jeffries-Matusita (J-M) measure. In first stage, we select a feature subset with minimal redundancy maximal relevance criteria. In second stage, we select further a feature subset from which obtained in first stage by maximizing J-M distance. Multiple Kernel Learning (MKL) and Ensemble Learning (EL) are promising family of machine learning algorithms and have been applied extensively in HSI classification. Many MKL methods often formulate the problem as an optimization task. To avoid solving the complicated optimization problem, this paper presents an ensemble learning framework, SMKB (Stochastic Multiple Kernel Boosting), which applies Adaptive Boosting (AdaBoost) and stochastic approach to learning multiple kernel-based classifier for multi-class classification problem. We examine empirical performance of proposed approach on benchmark hyperspectral classification data set in comparison with various state-of-the-art algorithms. Experimental results demonstrate that the proposed method obtains better feature subsets and is more effective and efficient than classical methods.
