Classification of Land Use in Farming Area Based on Random Forest Algorithm

Abstract:

Land use classification plays an important role in adjusting land structure and developing land resources reasonably, especially in the farming area. The objective of this research is to choose an appropriate method to classify land use type in the farming area. A new classification method, random forest (RF) classifier, was applied to make land use mapping in agricultural cultivation region with multisource information, including multiseasonal spectrum, texture and topographic information. The best classification scheme was chosen to extract land use information, and RF algorithm was used to reduce the dimension of characteristics variables. The RF algorithm, support vector machine, and maximum likelihood classification were used to map agricultural land use, and the applicability of these three different classification methods was analyzed. The result shows that RF classification of land use classification with multisource information effects best, the overall accuracy and Kappa coefficient are 85.54% and 0.8359 respectively. Feature selection method from RF algorithm can effectively reduce the data dimension and ensure the accuracy of classification at the same time. Compared with these three classification methods, RF algorithm performs the highest overall accuracy of 81.08%, which is respectively 9.46% and 5.27% higher than support vector machine and maximum likelihood classification. It is an effective scheme that makes land use classification in the farming area using RF classifier with multi-source information. It provides a fast and feasible method for the division of land use types.