Research on semi-supervised learning for hyperspectral remote sensing imaging classification base on confidence entropy

被引：0

作者：

Wang, Chunyang ^{[1
,2
]}

Xu, Zhifang ^{[2
]}

Wang, Shuangting ^{[2
]}

Zhang, Hebing ^{[2
]}

Chen, Zhichao ^{[2
]}

机构：

[1] Henan Polytech Univ, Natl Adm Surveying Mapping & Geoinformat, Key Lab Mine Spatial Informat Technol, Jiaozuo 454003, Peoples R China

[2] Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454003, Peoples R China

来源：

2016 IEEE 22ND INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS) | 2016年

关键词：

hyperspectral image; image classification; semi-supervised learning; posterior probability; confidence entropy; S EVIDENCE THEORY; 3; DECADES; FUSION;

D O I：

10.1109/ICPADS.2016.163

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The research of Hyperspectral classification is the hotpots at present. In this article, an effective semi-supervised classification method was proposed for hyperspectral image based on confidence entropy. The experimental results show that the proposed method can effectively improve the accuracy of classification and obtain better classification results for hyperspectral image data using few labeled samples.

引用

页码：1225 / 1228

页数：4

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