Earticle

초록

영어

Hyperspectral image classification has received an increasing amount of interest in recent years. However, when representing pixels as vectors, the dimensionality of feature space is high, which causes “curse of dimensionality” problem. In this paper, in order to alleviate the impact of above problem, a manifold sparse coding method is proposed. Firstly, matrix decomposition technique is used to find a concept set and calculates relative data projection in the concept set. Secondly, manifold learning regularization is imported into objective function to capture the intrinsic geometric structure in the data. Finally, LASSO regularization is used to obtain sparse representation of data projection. 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. Hyperspectral Image Cube
 3. Manifold Sparse Coding
  3.1. Matrix Decomposition Technology
  3.2. Manifold Learning Regularization
  3.3. LASSO Regularization
 4. Experimental Results
 5. Conclusions
 References

키워드

저자

• Yanbin Peng [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China ] corresponding Author
• Zhijun Zheng [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China ]
• Jiming Li [ Department of Forensic Science, Zhejiang Police College, Hhangzhou, China ]
• Zhigang Pan [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China ]
• Xiaoyong Li [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China ]
• Zhinian Zhai [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605