Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-8987
Main Title: Exploiting SAR Tomography for Supervised Land-Cover Classification
Author(s): D’Hondt, Olivier
Hänsch, Ronny
Wagener, Nicolas
Hellwich, Olaf
Type: Article
Language Code: en
Abstract: 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).
URI: https://depositonce.tu-berlin.de/handle/11303/9996
http://dx.doi.org/10.14279/depositonce-8987
Issue Date: 5-Nov-2018
Date Available: 11-Sep-2019
DDC Class: 006 Spezielle Computerverfahren
Subject(s): SAR tomography
land-cover classification
feature extraction
random forests
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Remote Sensing
Publisher: MDPI
Publisher Place: Basel
Volume: 10
Issue: 11
Article Number: 1742
Publisher DOI: 10.3390/rs10111742
EISSN: 2072-4292
Appears in Collections:FG Computer Vision & Remote Sensing » Publications

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