Evaluation of tree creation methods within random forests for classification of PolSAR images

Random Forests and their many variations developed to one of the most successful instruments to automatically analyse image data. One of the most crucial parts is the definition and selection of node tests within the individual trees, which among other things allow for trade-offs between accuracy and computational load. This paper discusses several different approaches to test creation and compares them based on their classification performance on polarimetric synthetic aperture radar data. The experiments show that selecting the best out of multiple randomly generated node tests leads to the highest accuracy with the smallest computational effort.