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One of the most challenges in the remote sensing applications is Hyperspectral image classification. Hyperspectral image classification accuracy depends on the number of classes, training samples and features space dimension. The classification performance degrades to increase the number of classes and reduce the number of training samples. The increase in the number of feature follows a considerable rise in data redundancy and computational complexity leads to the classification accuracy confusion. In order to deal with the Hughes phenomenon and using hyperspectral image data, a hierarchical algorithm based on SVM is proposed in this paper. In the proposed hierarchical algorithm, classification is accomplished in two levels. Firstly, the clusters included similar classes is defined according to Euclidean distance between the class centers. The SVM algorithm is accomplished on clusters with selected features. In next step, classes in every cluster are discriminated based on SVM algorithm and the fewer features. The features are selected based on correlation criteria between the classes, determined in every level, and features. The numerical results show that the accuracy classification is improved using the proposed Hierarchical SVM rather than SVM. The number of bands used for classification was reduced to 50, while the classification accuracy increased from 73% to 80% with applying the conventional SVM and the proposed Hierarchical SVM algorithm, respectively.

KEYWORDS

Feature Reduction Methods, Clustering Methods, Hyperspectral Image Classification, Support Vector Machine

Cite this paper

Hosseini, L. and Shaghaghi Kandovan, R. 2017 Hyperspectral Image Classification Based on Hierarchical SVM Algorithm for Improving Overall Accuracy. Advances in Remote Sensing, 6, 66-75. doi: 10.4236-ars.2017.61005.





Autor: Lida Hosseini, Ramin Shaghaghi Kandovan

Fuente: http://www.scirp.org/



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