Semi-supervised Hyperspectral Image Classification with Graphs

被引：8
|
作者：
Bandos, Tatyana V. [1 ]
Zhou, Dengyong [2 ]
Camps-Valls, Gustavo [1 ]
机构：
[1] Univ Valencia, Dept Elect Engn, C Dr Moliner 50, E-46100 Valencia, Spain
[2] NEC Lab America, Princeton, NJ 08540 USA
来源：
2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8 | 2006年
关键词：
D O I：
10.1109/IGARSS.2006.996
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to exploit the spatial/contextual information in the images through composite kernels. The proposed method produces smoother classifications with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Good accuracy in high dimensional spaces and low number of labeled samples (ill-posed situations) are produced as compared to standard inductive support vector machines.
引用
收藏
页码：3883 / +
页数：2
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