Earticle

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

키워드

저자

• Yanbin Peng [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China ]
• Zhigang Pan [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China ]
• Zhijun Zheng [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China ]
• Xiaoyong Li [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605