Random feature selection for decision tree classification of multi-temporal SAR data

The accuracy of supervised land cover classifications depends on variables like the chosen algorithm, adequate training data and the selection of features. It has been shown that classification results can be improved by classifier ensembles. In the present study decision trees have been generated with random selections of all available features and combined into such a multiple classifier. The influence of the number of selected features and the size of the multiple classifiers on classification accuracy is investigated using a set of 14 SAR images. Results of multiple classifiers are always better than those of a decision tree based on all available features. Maximum accuracies were achieved with multiple classifiers that use decision trees based on 70% of the available features. The visual inspection of produced maps underlines the high quality of the results. The area is classified into homogeneous fields with little noise, only.