원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.2
2016.02
pp.183-192
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Hyperspectral image mostly have very large amounts of data which makes the computational cost and subsequent classification task a difficult issue. Firstly, to solve the problem of computational complexity, spectral clustering algorithm is imported to select efficient bands for subsequent classification task. Secondly, due to lack of labeled training sample points, this paper proposes a new algorithm that combines support vector machines and Bayesian classifier to create a discriminative/generative hyperspectral image classification method using the selected features. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.
목차
Abstract
1. Introduction
2. Band Selection Algorithm
3. Fusion of SVM and Bayesian
3.1. Hyperspectral Image Classifier based on SVM
3.2. Hyperspectral Image Classification based on Bayesian
3.3. Classifier Fusion
4. Experiments and Results
4.1. The Comparison of Average Classification Precision in Noiseless Environment
4.2. The Comparison of Average Classification Precision in Noise Environment
5. Conclusions
Acknowledgements
References
1. Introduction
2. Band Selection Algorithm
3. Fusion of SVM and Bayesian
3.1. Hyperspectral Image Classifier based on SVM
3.2. Hyperspectral Image Classification based on Bayesian
3.3. Classifier Fusion
4. Experiments and Results
4.1. The Comparison of Average Classification Precision in Noiseless Environment
4.2. The Comparison of Average Classification Precision in Noise Environment
5. Conclusions
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보