Title :
An improved random decision trees algorithm with application to land cover classification
Author :
Xu, Haiwei ; Yang, Minhua ; Liang, Liang
Author_Institution :
Sch. of Info-Phys. & Geomatics Eng., Central South Univ., Changsha, China
Abstract :
Random decision trees algorithm, which is no need to count images and has good portability, is a great prospective method for land cover remote sensing classification at present. An improved random decision trees algorithm with application to land cover remote sensing classification was proposed in this paper. Firstly, in accordance with the low operation efficiency of random decision trees algorithm, 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 object, 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 87.53% and Kappa coefficient is up to 0.8524. By taking extreme imbalanced decision trees, node impurity and distinguishing sample types into consideration, the improved random decision trees algorithm can improve the efficiency and accuracy of land cover remote sensing classification.
Keywords :
decision trees; geophysical image processing; image classification; maximum likelihood estimation; terrain mapping; vegetation mapping; ALOS images; China; Guangdong Province; Kappa coefficient; Longmen city; extreme imbalanced decision trees; land cover classification; land cover remote sensing classification; maximum likelihood classification; node impurity; random decision trees algorithm; sample types; tree balance factor; Accuracy; Algorithm design and analysis; Classification algorithms; Classification tree analysis; Remote sensing; Training; Automatic Classification; Land Cover Classification; Pattern Classification; Random Decision Trees; Remote Sensing;
Conference_Titel :
Geoinformatics, 2010 18th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-7301-4
DOI :
10.1109/GEOINFORMATICS.2010.5567531