Joint probability-based classifier based on vine copula method for land use classification of multispectral remote sensing data

Abstract

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.