最优代表向量法及其在冰川分类中的应用

发布时间：2018-01-24 14:51

  本文关键词： 高光谱遥感 图像分类 最优代表向量 密度峰值聚类 冰川分类　出处：《北京理工大学学报》2017年10期 　论文类型：期刊论文

【摘要】：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.
[Abstract]:Aiming at the problem that the classification accuracy of hyperspectral remote sensing images is poor due to the heterospectral phenomenon of the same objects and the poor representation of samples and the setting of artificial parameters in the classification process, an optimal representative vector classification method for the optimization of sample sets is proposed. The samples in the region of interest are purified by peak density clustering, and the mean vector set of each kind of ground objects is selected by membership degree clustering, and the optimal representative vector set is obtained as the center vector of this kind of feature. Finally, the classification is based on the distance criterion. Through comparative experiments, the overall classification accuracy of this algorithm is higher than 90 points, which shows that the optimal representative vector classification method can effectively eliminate the impact of sample differences. Improve the accuracy of glacier classification.
【作者单位】： 北京科技大学自动化学院;
【基金】：高分辨率对地观测系统重大专项基金资助项目 “十三五”武器装备预研领域基金资助项目 中央高校基本科研业务费专项资金资助项目(FRF-TP-15-117A1) 中国博士后科学基金资助项目(2016M600922)
【分类号】：TP751
【正文快照】： 冰川中蕴含着极其丰富的淡水资源,对全球气候变化与生态平衡起着关键性的作用,但大部分冰川地处偏僻,面积宽广,所以遥感技术被广泛应用于大尺度的冰川监测中[1].近年来,全球流域冰川监测研究主要采用Landsat ETM+、SPOT5、ASTER等多光谱遥感影像,而高光谱遥感影像应用较少.然，

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