Hyperspectral Image Classification Based on Hierarchical SVM Algorithm for Improving Overall Accuracy
| Autor: | Ramin Shaghaghi Kandovan, Lida Hosseini |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computational complexity theory
Remote sensing application 0211 other engineering and technologies 02 engineering and technology computer.software_genre 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 021101 geological & geomatics engineering General Environmental Science Mathematics business.industry Hyperspectral imaging Pattern recognition Class (biology) Support vector machine Euclidean distance ComputingMethodologies_PATTERNRECOGNITION Data redundancy Computer Science::Computer Vision and Pattern Recognition General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence Data mining business Algorithm computer |
| Zdroj: | Advances in Remote Sensing. :66-75 |
| ISSN: | 2169-2688 2169-267X |
| DOI: | 10.4236/ars.2017.61005 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|