属类概率距离构图的半监督高光谱图像分类
收稿日期: 2013-01-15
修回日期: 2014-03-26
网络出版日期: 2014-12-02
基金资助
国家自然科学基金项目资助;中央高校基本科研业务费专项资金资助
Graph based Semi-Supervised Learning with Class-Probability Distance for Hyperspectral Remote Sensing Image Classification
Received date: 2013-01-15
Revised date: 2014-03-26
Online published: 2014-12-02
邵远杰 吴国平 马丽 . 属类概率距离构图的半监督高光谱图像分类[J]. 测绘学报, 2014 , 43(11) : 1182 -1189 . DOI: 10.13485/j.cnki.11-2089.2014.0182
A class-probability distance based semi-supervised learning method is proposed for hyperspectral remote sensing image classification. In the method, Sparse Representation based Classification (SRC) is adopted for estimating the class-probability of unlabeled sample. Then a distance metric that describes the data similarity is developed based on the estimated class-probability. With this new distance metric, the distance between samples of different classes is enlarged effectively, and the neighbors of each sample can contain more samples belonging to the same class. Finally, this distance metric is applied to Linear Neighborhood Propagation and Label Propagation algorithms. Experimental results using Hyperion and AVIRIS hyperspectral remote sensing images show that the approach outperforms the existing semi-supervised learning methods in terms of classification accuracy.
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