摘要
针对单一的属性剖面特征难以全面反映地物特性的问题,提出一种融合多种属性剖面特征的高光谱影像分类方法。首先提取高光谱影像4种形态学属性剖面特征,接着基于4组剖面特征对高光谱影像进行分类,获得样本的预测类别和后验概率估计值;在此基础上,计算不同特征的分类可靠性以及重要度权值,二者结合建立基于概率的决策融合模型,获得高光谱影像的最终分类结果。高光谱影像分类实验表明,所提融合算法的性能不但优于使用单个属性剖面特征的情况,也优于其他多种融合算法。
A new method for hyperspectral image classification is proposed.In the method,multi-attribute profiles are used and fused by a probabilistic fusion model.First,four types of attribute profile features are extracted from the reduced images which are generated by dimensionality reduction.Next,based on the four types of features,four classification results and the corresponding estimation of posterior probability are obtained.Then,the reliability and important weight of each feature are calculated based on the predicted labels and posterior probabilities.Finally,a probabilistic fusion model is constructed using the reliabilities and weights of features,and the final classification results are obtained.The experiments of hyperspectral image classification show that the proposed method can not only achieve better performance than the methods using any of single features,but also outperform other fusion methods.
作者
陈军丽
黄睿
CHEN Junli;HUANG Rui(School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China)
出处
《遥感信息》
CSCD
北大核心
2019年第2期69-74,共6页
Remote Sensing Information
关键词
高光谱影像
分类
属性剖面
概率融合
预测类别
hyperspectral image
classification
attribute profile
probabilistic fusion
predicted label
