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Indexed by:期刊论文

Date of Publication:2016-02-01

Journal:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

Included Journals:SCIE、EI、Scopus

Volume:13

Issue:2

Page Number:177-181

ISSN No.:1545-598X

Key Words:Collaborative representation (CR); hyperspectral image (HSI) classification; joint model; support vector machine (SVM)

Abstract:In this letter, we propose a novel hyperspectral image (HSI) classification method based on the joint collaborative representation (JCR) and support vector machine (SVM) models with decision fusion. First, motivated by the joint model, we adopt a JCR model to deal with HSI classification and develop an effective method to learn contextual basis vectors for the JCR model. Second, the mid-features are first extracted based on representation coefficients obtained by the JCR method and then used to train a multiclass SVM classifier. After that, we exploit a multiplicative fusion rule to combine the JCR and SVM models. We conduct numerous experiments to evaluate our method in comparison with other algorithms. The experimental results on three standard data sets demonstrate that our method achieves better performance than other competing ones.