SEMI-SUPERVISED CONDITIONAL RANDOM FIELD FOR HYPERSPECTRAL REMOTE SENSING IMAGE CLASSIFICATION

被引：4
|
作者：
Wu, Junfeng [1 ]
Jiang, Zhiguo
Zhang, Haopeng
Cai, Bowen
Wei, Quanmao
机构：
[1] Beihang Univ, Image Proc Ctr, Sch Astronaut, Beijing 100191, Peoples R China
来源：
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年
基金：
中国国家自然科学基金;
关键词：
CRF; semi-supervised; hyperspectral; remote sensing; classification; CONSTRAINT;
D O I：
10.1109/IGARSS.2016.7729675
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Conditional Random Field(CRF) has been successfully applied to the hyperspectral image classification. However, it suffers from the availability of large amount of labeled pixels, which is labor- and time-consuming to obtain in practice. In this paper, a semi-supervised CRF(ssCRF) is proposed for hyperspectral image classification with limited labeled pixels. Laplacian Support Vector Machine(LapSVM), after extended into the composite kernel type, is defined as the association potential. And the Potts model is utilized as the interaction potential. The ssCRF is evaluated on the two benchmarks and the results show the effectiveness of ssCRF.
引用
收藏
页码：2614 / 2617
页数：4
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