Hyperspectral Image Classification via JCR and SVM Models With Decision Fusion

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Chunjuan Bo, Huchuan Lu, Dong Kai Wang
Journal/Conference Name IEEE Geoscience and Remote Sensing Letters
Paper Category
Paper 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.
Date of publication 2016
Code Programming Language MATLAB
Comment

Copyright Researcher 2021