Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/608939
Title: Hyperspectral Image Classification via JCR and SVM Models With Decision Fusion
Authors: Chunjuan Bo;Huchuan Lu;Dong Wang
subject: Collaborative representation (CR)|joint model|hyperspectral image (HSI) classification|support vector machine (SVM)
Year: 2016
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/140696
http://localhost/handle/Hannan/608939
ISSN: 1545-598X
1558-0571
volume: 13
issue: 2
Appears in Collections:2016

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Title: Hyperspectral Image Classification via JCR and SVM Models With Decision Fusion
Authors: Chunjuan Bo;Huchuan Lu;Dong Wang
subject: Collaborative representation (CR)|joint model|hyperspectral image (HSI) classification|support vector machine (SVM)
Year: 2016
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/140696
http://localhost/handle/Hannan/608939
ISSN: 1545-598X
1558-0571
volume: 13
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360896.pdf840.97 kBAdobe PDFThumbnail
Preview File
Title: Hyperspectral Image Classification via JCR and SVM Models With Decision Fusion
Authors: Chunjuan Bo;Huchuan Lu;Dong Wang
subject: Collaborative representation (CR)|joint model|hyperspectral image (HSI) classification|support vector machine (SVM)
Year: 2016
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/140696
http://localhost/handle/Hannan/608939
ISSN: 1545-598X
1558-0571
volume: 13
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360896.pdf840.97 kBAdobe PDFThumbnail
Preview File