点击次数：

论文类型：期刊论文

发表时间：2016-02-01

发表刊物：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

收录刊物：SCIE、EI、Scopus

卷号：13

期号：2

页面范围：177-181

ISSN号：1545-598X

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

摘要：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.