Comparison of object-oriented remote sensing image classification based on different decision trees in forest area.

Geographic Object-Based Image Analysis (GEOBIA) was a product under the background of increasing high-resolution remote sensing data. How to improve the accuracy and efficiency of classification of high-resolution images is one of the important topics in image processing. After objects segmented multiscale by QuickBird image was classified, the efficiency of C5.0, C4.5, and CART decision trees in object-oriented classification of forest areas was analyzed. The accuracy of those three methods were compared with kNN method. The eCognition software was used to multiscale segmentation of remote sensing images, with the result showing that 90 and 40 were the optimal scales. After separating vegetation and non-vegetation at 90 scale, 21 features such as spectrum, texture and shape of different vegetation types were extracted at 40 scale, knowledge mining was carried out by using C5.0, C4.5 and CART decision tree algorithms respectively, and classification rules were automatically established. The vegetation area was classified based on the classification rules and the classification accuracy of different methods was compared. The results showed that the classification accuracy based on decision-tree was higher than that of the traditional kNN method. The accuracy of C5.0 method was the best, with the overall accuracy and Kappa coefficient reaching 90.0% and 0.87, respectively. The decision tree algorithm could effectively improve the accuracy in classification of forest species. The Boosting algorithm of the C5.0 decision tree had the most significant improvement on the classification.