Next Article in Journal
Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil
Next Article in Special Issue
Towards a 20 m Global Building Map from Sentinel-1 SAR Data
Previous Article in Journal
A Novel Method of Generating Deformation Time-Series Using Interferometric Synthetic Aperture Radar and Its Application in Mexico City
Previous Article in Special Issue
Multi-Stream Convolutional Neural Network for SAR Automatic Target Recognition
Open AccessArticle

Exploiting SAR Tomography for Supervised Land-Cover Classification

Technische Universität Berlin, MAR6-5, Marchstr. 23, 10587 Berlin, Germany
European Space Agency, Largo Galileo Galilei 1, 00044 Frascati, Italy
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1742;
Received: 30 August 2018 / Revised: 26 October 2018 / Accepted: 1 November 2018 / Published: 5 November 2018
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). View Full-Text
Keywords: SAR tomography; land-cover classification; feature extraction; random forests SAR tomography; land-cover classification; feature extraction; random forests
Show Figures

Graphical abstract

MDPI and ACS Style

D’Hondt, O.; Hänsch, R.; Wagener, N.; Hellwich, O. Exploiting SAR Tomography for Supervised Land-Cover Classification. Remote Sens. 2018, 10, 1742.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop