Ensemble Methods for Classification of Hyperspectral Data
2008
The classification of hyperspectral data is addressed using a classifier ensemble based on Support Vector Machines (SVM). First of all, the hyperspectral data set is decomposed into few sources according to the spectral bands correlation. Then, each source is treated separately and classified by an SVM classifier. Finally, all outputs are used as inputs for the final decision fusion, performed by an additional SVM classifier. The results of experiments, clearly show that the proposed SVM-based decision fusion outperforms a single SVM classifier in terms of overall accuracies.
Keywords:
- Contextual image classification
- Random forest
- Structured support vector machine
- Artificial neural network
- Support vector machine
- Machine learning
- Margin classifier
- Ensemble learning
- Hyperspectral imaging
- Computer science
- Artificial intelligence
- Pattern recognition
- Data mining
- Classifier (linguistics)
- Kernel (linear algebra)
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