Exploiting SAR Tomography for Supervised Land-Cover Classification

In this paper, we provide the first in-depth evaluation of exploiting Tomographic Synthetic Aperture Radar (TomoSAR) for the task of supervised land-cover classification. Our main contribution is the design of specific TomoSAR features to reach this objective. In particular, we show that classification based on TomoSAR significantly outperforms PolSAR data provided relevant features are extracted from the tomograms. We also provide a comparison of classification results obtained from covariance matrices versus tomogram features as well as obtained by different reference methods, i.e., the traditional Wishart classifier and the more sophisticated Random Forest. Extensive qualitative and quantitative results are shown on a fully polarimetric and multi-baseline dataset from the E-SAR sensor from the German Aerospace Center (DLR).