A cascaded ensemble classifier for object segmentation in high resolution polarimetric SAR data

The paper proposes a novel approach to object classification and segmentation in multi-channel (e.g. polarimetric) SAR data. The classifier is intended for particularly difficult problems, where objects of interest exhibit a high degree of radiometric, polarimetric and geometric heterogeneity, both within individual object instances and across the object category as a whole. Classification is based on a non-parametric characterization of scene contents that avoids model assumptions liable to fail in this scenario. The classifier structure is based on a combination of techniques developed for related problems in computer vision: the cascade architecture helps breaking down the problem into manageable stages while random forests provide a powerful framework for learning and combining discriminative classification rules. In addition, scale space techniques explicitly introduce non-local, contextual and geometric information into the classification process. Preliminary results illustrate the potential of the proposed approach with respect to the task of building segmentation in dual-polarized TerraSAR-X data.