Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data

ABSTRACT Fully polarimetric synthetic aperture radar (PolSAR) Earth Observations showed great potential for mapping and monitoring agro-environmental systems. Numerous polarimetric features can be extracted from these complex observations which may lead to improve accuracy of land-cover classification and object characterization. This article employed two well-known decision tree ensembles, i.e. bagged tree (BT) and random forest (RF), for land-cover mapping from PolSAR imagery. Moreover, two fast modified decision tree ensembles were proposed in this article, namely balanced filter-based forest (BFF) and cost-sensitive filter-based forest (CFF). These algorithms, designed based on the idea of RF, use a fast filter feature selection algorithms and two extended majority voting. They are also able to embed some solutions of imbalanced data problem into their structures. Three different PolSAR datasets, with imbalanced data, were used for evaluating efficiency of the proposed algorithms. The results indicated that all the tree ensembles have higher efficiency and reliability than the individual DT. Moreover, both proposed tree ensembles obtained higher mean overall accuracy (0.5–14% higher), producer’s accuracy (0.5–10% higher), and user’s accuracy (0.5–9% higher) than the classical tree ensembles, i.e. BT and RF. They were also much faster (e.g. 2–10 times) and more stable than their competitors for classification of these three datasets. In addition, unlike BT and RF, which obtained higher accuracy in large ensembles (i.e. the high number of DT), BFF and CFF can also be more efficient and reliable in smaller ensembles. Furthermore, the extended majority voting techniques could outperform the classical majority voting for decision fusion.