Parameter optimization strategy of random forest algorithm for land use classification

Abstract

When using the random forest algorithm to classify remote sensing images of each target year in the study area, the number of decision trees and the maximum number of features for constructing the optimal model of decision trees have a great influence on the accuracy of the random forest classification results. Based on this, this paper proposes an adaptive parameter tuning strategy based on GridSearchCV to improve the random forest algorithm. The method can select the best parameters according to different sample data and study area conditions. By comparing with unoptimized random forest, decision tree, and support vector machine algorithms, the results suggest that: the optimized random forest algorithm has good classification accuracy, and the overall accuracy and Kappa coefficient of classification results are above 0.90.