Earticle

초록

영어

Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most “valuable” samples to be labeled from massive unlabeled sample set. Secondly, a L1 norm distance measuring was used to further select and remove outliers and redundant data from the sample set to be labeled. Finally, based on originally labeled samples and screened samples, PLSSVM was gained through training. Experimental results on classification of ROSIS hyperspectral remote sensing images show that the overall accuracy and Kappa coefficient of the proposed classification method reach higher accuracy respectively. The proposed method can obtain higher classification accuracy with few training samples, which is much applicable to classification problem of remote sensing images.

목차

Abstract
 1. Introduction
 2. Plssvm
 3. Active Learning based on Hybrid Entropy and L1 Norm
 4. Algorithm Steps
 5. Experiment and Analysis
 6. Conclusions
 References

키워드

저자

• Chen Xiao-hui [ Information Engineering School, Yulin University, Yulin 719000, Shaanxi, China Shanghai Jiaotong University, 200230, Shanghai, China ]
• Gao Yan [ Information Engineering School, Yulin University, Yulin 719000, Shaanxi, China Shanghai Jiaotong University, 200230, Shanghai, China ]
• Li Jun-yi [ Information Engineering School, Yulin University, Yulin 719000, Shaanxi, China Shanghai Jiaotong University, 200230, Shanghai, 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