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Potentials of TanDEM-X Interferometric Data for Global Land Classification

Martone, Michele and Rizzoli, Paola and Krieger, Gerhard (2016) Potentials of TanDEM-X Interferometric Data for Global Land Classification. TerraSAR-X Science Team Meeting, 2017-10-17 - 2017-10-20, Munich, Germany.

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TanDEM-X has opened a new era in spaceborne radar remote sensing: since 2010, the two satellites TerraSAR-X and TanDEM-X fly in a closely controlled formation at a distance of a few hundred meters, acting as a large single pass radar interferometer with flexible along- and cross-track baseline selection. The primary objective of the mission is the generation of a global, consistent, and high-precision DEM in bistatic configuration, which is going to be finalized by the end of 2016. About half a million high resolution bistatic InSAR scenes have been acquired, covering all the Earth’s land masses. TanDEM-X provides therefore the community with a unique, manifold, and highly accurate data set to be exploited for a broad range of commercial and scientific applications. One of the main parameters for asserting the quality of interferometric products is the interferometric coherence between the monostatic and the bistatic images. Global mosaics of such a quantity have been operationally used to quickly analyze the mission performance and to consequently optimize the acquisition planning. Moreover, large scale mosaics of the interferometric coherence represent a valid starting point for further scientific applications and for the generation of value-added products from the TanDEM-X global data set. Its properties are strongly influenced by the specific land-cover type of the illuminated target on ground and can be therefore exploited for classification purposes. Over forested regions the presence of multiple scatterers at different heights and within a single resolution cell results in an increase of the interferometric phase uncertainty due to penetration, whose amount depends on several factors, such as the acquisition geometry, the sensor parameters, and the canopy density. Hence, from each bistatic TanDEM-X image the volume decorrelation contribution can be isolated from the interferometric coherence and used for classification purposes. In the field of glaciology, the characterization of snow facies is an interesting and open topic. The combination of the information coming from both backscatter intensity and interferometric coherence can be exploited for classifying different kinds of snow, dominated by different scattering mechanisms. An example is the location of different snow facies of the Greenland ice sheet. The aim of this paper is to introduce the potentials of the TanDEM-X interferometric data set for global land cover classification, focusing on the discrimination between forest/non-forest and on the classification of snow facies.

Item URL in elib:https://elib.dlr.de/108339/
Document Type:Conference or Workshop Item (Speech)
Title:Potentials of TanDEM-X Interferometric Data for Global Land Classification
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Martone, MicheleUNSPECIFIEDhttps://orcid.org/0000-0002-4601-6599UNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Krieger, GerhardUNSPECIFIEDhttps://orcid.org/0000-0002-4548-0285UNSPECIFIED
Date:October 2016
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:TanDEM-X, volume decorrelation, classification, forest map, Greenland ice sheet
Event Title:TerraSAR-X Science Team Meeting
Event Location:Munich, Germany
Event Type:international Conference
Event Dates:2017-10-17 - 2017-10-20
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Projekt TanDEM-X (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > Spaceborne SAR Systems
Microwaves and Radar Institute > Radar Concepts
Deposited By: Martone, Michele
Deposited On:29 Nov 2016 08:50
Last Modified:16 Jan 2017 18:42

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