Joint probability-based classifier based on vine copula method for land use classification of multispectral remote sensing data
Joint probability-based classifier based on vine copula method for land use classification of multispectral remote sensing data
复制标题
基于vine copula方法的联合概率分类器用于多光谱遥感数据土地利用分类
DOI：
10.1007/s12145-020-00487-0
复制
发表时间：
2020-07
影响因子：
2.8
通讯作者：
Zhifeng Yang
中科院分区：
文献类型：
--
作者：
Yunlong Zhang;Xuan Wang;Dan Liu;Chunhui Li;Qiang Liu;Yanpeng Cai;Yujun Yi;Zhifeng Yang
土地利用分类是监测和预测区域发展格局以及规划和调节土地利用的基础。提出了一种基于联合概率的多光谱遥感数据土地利用分类器，并在白洋淀地区进行了应用。该分类器基于vine copula方法，适用于处理土地分类及其随机变量不一定服从预定义分布的不确定性。将所提分类器与广泛使用的最大似然分类器的结果进行比较，结果表明，所提分类器对多光谱遥感数据的土地利用分类精度更高。与最大似然分类器的权变矩阵相比，藤丛分类器的权变矩阵对农村土地（浅水）生产者（用户）的准确率提高了29.4%（30.0%）。该分类器增加了浅水面积，显著减少了农村土地面积。主要原因是最大似然分类器分类性能差，将浅水像元误分类为农村土地。研究结果表明，藤蔓联结分类器的分类性能优于传统的最大似然分类器，其应用可以促进遥感数据的充分利用。
Land use classification is fundamental both for monitoring and predicting regional development patterns and for planning and regulating land use. This research proposed a joint probability-based classifier for land use classification of multispectral remote sensing data and applied it to the Lake Baiyangdian region of North China. This classifier, based on the vine copula method, was suitable for dealing with the uncertainties of land classification and its random variables that did not necessarily obey predefined distributions. Comparison of the results obtained using the proposed classifier with those derived using the widely used maximum likelihood classifier indicated that the accuracy of land use classification of multispectral remote sensing data was higher with the proposed classifier. Compared with the contingency matrix of the maximum likelihood classifier, that of the vine copula classifier showed an increase in the producer’s (user’s) accuracy of rural land (shallow water) of 29.4% (30.0%). The proposed classifier increased the shallow water area and significantly reduced the area of rural land. The main reason was the maximum likelihood classifier had poor classification performance, misclassifying pixels of shallow water as rural land. The findings of this study demonstrated that the vine copula classifier performs better than the traditional maximum likelihood classifier and that its application could promote full utilization of remotely sensed data.
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