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.

土地用途分类对于监测和预测区域发展模式以及规划和调整土地用途都是至关重要的。该研究提出了一种基于概率的联合分类器，用于多光谱遥感数据的土地利用分类，并将其应用于华北白洋淀地区。该分类器基于藤蔓copula方法，适用于处理土地分类的不确定性及其不一定遵循预定分布的随机变量。通过使用建议的分类器与使用广泛使用的最大似然分类器得出的结果进行比较，结果表明，使用建议的分类器对多光谱遥感数据进行土地利用分类的准确性更高。与最大似然分类器的权变矩阵相比，葡萄系分类器的权变矩阵显示出生产者（使用者）对农村土地（浅水）的准确性提高了29.4％（30.0％）。拟议的分类器增加了浅水区域，并大大减少了农村土地的面积。主要原因是最大似然分类器的分类性能较差，将浅水像素分类为农村土地。这项研究的结果表明，藤蔓copula分类器的性能优于传统的最大似然分类器，并且其应用可以促进遥感数据的充分利用。拟议的分类器增加了浅水区域，并大大减少了农村土地的面积。主要原因是最大似然分类器的分类性能较差，将浅水像素分类为农村土地。这项研究的结果表明，藤蔓copula分类器的性能优于传统的最大似然分类器，并且其应用可以促进遥感数据的充分利用。拟议的分类器增加了浅水区域，并大大减少了农村土地的面积。主要原因是最大似然分类器的分类性能较差，将浅水像素分类为农村土地。这项研究的结果表明，藤蔓copula分类器的性能要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。