Supervised classification from TomoSAR data

Abstract:
In this paper, we propose to study the potential of supervised classification from tomographic SAR data without using any polarimetric information. We show that it is possible to achieve comparable results and even outperform classification results from polarimetric data by carefully designing hand-crafted features which can be extracted either from multi-baseline single polarization covariance matrices or from tomograms obtained after tomographic focusing. Our experiments show a significant gain in the classification accuracy, especially on challenging classes such as heterogeneous city and road.