图Laplacian和自训练用于高光谱数据半监督波段选择  

Semi-supervised Band Selection Based on Graph Laplacian and Self-training for Hyperspectral Data Classification

作　　者：黄睿[1] 吕智强[1] 

机构地区：[1]上海大学通信与信息工程学院,上海200072

出　　处：《数据采集与处理》2014年第6期981-985,共5页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61001162)资助项目

摘　　要：波段选择是数据降维的有效手段,但有限的标记样本影响了监督波段选择的性能。提出一种利用图Laplacian和自训练策略实现半监督波段选择的方法。该方法首先定义基于图的半监督特征评分准则以产生初始波段子集,接着在该子集基础上进行分类,采用自训练策略将部分可信度较高的非标记样本扩展至标记样本集合,再用特征评分准则对波段子集进行更新。重复该过程,获得最终波段子集。高光谱波段选择与分类实验比较了多种非监督、监督和半监督方法,实验结果表明所提算法能选择出更好的波段子集。Band selection is an effective method for dimensionality reduction.However,the information from the small size of labeled samples usually misleads the supervised band selection.A semi-supervised band selection method based on graph Laplacian and self-training idea is proposed.The method first puts forward the graph-based semi-supervised criterion for feature ranking to generate the initial band subset.The graph Laplacian used in the criterion is refined with aid of the label information.Then,the supervised classifications are carried out based on the band subset and some unlabeled samples with higher confidence values are added into the labeled sample set.Afterwards the band subset is updated according to the feature ranking based on the newly generated labeled and unlabeled data,and is used for classification.The process repeats to obtain the final subset.The experiments on hyperspectral data sets are carried out compare several unsupervised,supervised and semi-supervised band selection methods.Results show that the proposed method can produce the band subset with better performance.

关 键 词：高光谱数据分类 波段选择 半监督学习 图Laplacian 自训练 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]