Earticle

초록

영어

In this paper we have proposed a new marker selection technique using Support Vector Machine over-fitting. Markers are the most reliable pixels in a class. We used our proposed technique to do classification of hyperspectral image with very low training samples, as low as one pixel per class. We have used both spectral and spatial information to improve the classification results. The spatial information is extracted using Extended Morphological Profiles with duality. Nonparametric supervised feature extraction methods are used to eliminate the redundant and irrelevant information in both spatial and spectral domains. In the end we have done experimentation to verify our proposed approach. The experimentation results show that when non-parametric weighted feature extraction method is used we get better classification results. The classification maps shows that even with just one training sample per class we still can get a reliably reasonable classification map.

목차

Abstract
 1. Introduction
 2. Marker Selection using SVM Over-fitting
 3. Non-Parametric Supervised Feature Extraction techniques
 4. Experimental Results
 5. Conclusion
 References

키워드

저자

• Farid Muhammad Imran [ Shaanxi Key Laboratory of Information Acquisition and Processing, Center for Earth Observation, School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710129, 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