最优代表向量法及其在冰川分类中的应用

针对同物异谱现象以及分类过程中样本代表性差、人工参数设置等原因导致高光谱遥感影像分类精度差的问题,提出了一种样本集优化的最优代表向量分类法,对感兴趣区中的样本进行密度峰值聚类提纯,并对每类地物提纯后样本的均值向量集进行隶属度聚类择优,获取最优代表向量集作为该类地物的中心向量,最终依据距离准则进行分类.通过对比实验验证,本文算法总体分类精度高于90%,表明最优代表向量分类法能够有效消除样本差异性的影响,提高冰川分类精度. 

Optimal Representative Vector Method and Its Application for Glacier Classification

The existence of synonyms spectrum phenomenon and the poor sample representativeness and parameter setting in the classification process result in the unstable classification result and poor classification accuracy of hyperspectral images. In order to solve this problem, a novel optimal representative vectors classification algorithm based on sample set optimization was proposed for glacier classification. This algorithm purified original samples in the ROI through an improved density peak clustering method, and selected the optimal representative vectors by clustering the mean vector set of optimized samples as the central vector of each object. Through experimental verification, this algorithm can effectively improve the classification accuracy of the glacier to 90%; it shows that the optimal representative vectors classification method can eliminate the impact of sample differences and improve glacier classification accuracy.