An Improved Random Decision Trees Algorithm with Application to Supervised Classification

An improved Random Decision Trees algorithm with application to land cover remote sensing classification was proposed in this paper.Firstly,an improved Random Decision Trees algorithm was presented by adding tree balance factor,setting node impurity and distinguishing sample types.Secondly,by taking the ALOS images of Longmen City of Guangdong Province in China as study area,the remote sensing classification was conducted using the improved Random Decision Trees algorithm.Finally,a comparison study was proceeded to compare the improved Random Decision Trees algorithm with Maximum Likelihood Classification method.The results indicate that the classification precision is improved from 81.46% to 92.45% and Kappa coefficient is up to 0.9091.The improved Random Decision Trees algorithm can improve the efficiency and accuracy of land cover remote sensing classification.