Earticle

초록

영어

This paper presents a new technique for marker selection called marker selection using skeletonization. Markers are the most reliable pixels that represent a particular class. Marker selection using skeletonization is further analysed to do classification of hyperspectral image with very low training samples, as low as one pixel per class. Both spatial and spectral information are used to improve the final classification accuracy. An Extended Morphological Profile with duality is used to extract spatial information. Furthermore, it is shown that by using the spatial and spectral information with non- parametric supervised feature extraction methods, better classification accuracy can be achieved even when very low training samples are available. The classification maps will be shown and discussed for very low training sample analysis using marker selection by skeletonization technique.

목차

Abstract
 1. Introduction
 2. Marker Selection Using Skeletonization
 3. Non-Parametric Supervised Feature Extraction
 4. Experimental Results
 5. Conclusion
 Acknowledgements
 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 ]
• Mingyi He [ 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 Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605