Research Article
CBERS-02 remote sensing data mining using decision tree algorithm
@INPROCEEDINGS{10.4108/wkdd.2008.2661, author={Xingping Wen and Guangdao Hu and Xiaofeng Yang}, title={CBERS-02 remote sensing data mining using decision tree algorithm}, proceedings={1st International ICST Workshop on Knowledge Discovery and Data Mining}, publisher={ACM}, proceedings_a={WKDD}, year={2010}, month={5}, keywords={}, doi={10.4108/wkdd.2008.2661} }- Xingping Wen
Guangdao Hu
Xiaofeng Yang
Year: 2010
CBERS-02 remote sensing data mining using decision tree algorithm
WKDD
ACM
DOI: 10.4108/wkdd.2008.2661
Abstract
In recent years, decision tree algorithms have been successfully used for land cover classification from remote sensing data. In this paper, CART (classification and regression trees) and C5.0 decision tree algorithms were used to CBERS-02 remote sensing data. Firstly, the remote sensing data was transformed using the Principal Component Analysis (PCA) and multiple-band algorithm. Then, the training data was collected from the combining total 20 processed bands. Finally, the decision tree was constructed by CART and C5.0 algorithm respectively. Comparing two results, the most important variables are clearly band3,4, band1,4 and band2,4. The depth of the CART tree is only two with the relative high accuracy. The classification outcome was calculated by CART tree. In order to validate the classification accuracy of CART tree, the Confusion Matrices was generated by the ground truth data collected using visual interpretation and the field survey and the kappa coefficient is 0.95.