Paper Info
Title
Radar Backscatter Modeling Based on Global TanDEM-X Mission Data.
Abstract
Radar backscatter knowledge is a valuable input for many remote sensing applications, which are based on synthetic aperture radar (SAR) systems. The amount of spaceborne data acquired within the TanDEM-X mission allows for the characterization of X-band backscatter on a global scale. The objective of this paper is to present a method for the characterization of radar backscatter behavior using a global statistical approach. The worldwide data set of images acquired within the TanDEM-X mission is taken into account, having the chance to exploit the unique high-quality topography information associated to it. The input measurements are differently assessed, by using a quality-based approach. A series of models can be derived, focusing on the backscatter dependence on polarization, incidence angle, ground classification, and seasonal time. The developed approach for the algorithm's verification is presented as well, together with some preliminary results obtained from TanDEM-X mission data. The generation of up-to-date backscatter models for X-band will provide a useful database for the development of a large number of scientific applications and for the optimization of future radar systems.
Year
DOI
Venue
2014
10.1109/TGRS.2013.2294352
IEEE T. Geoscience and Remote Sensing
Keywords
Field
DocType
backscatter,data acquisition,digital elevation models,electromagnetic wave polarisation,image classification,optimisation,radar imaging,remote sensing by radar,spaceborne radar,statistical analysis,synthetic aperture radar,X-band backscatter,global TanDEM-X mission data,global scale,global statistical approach,ground classification,incidence angle,optimization,polarization,quality-based approach,radar backscatter modeling,remote sensing applications,seasonal time,spaceborne data acquisition,synthetic aperture radar,Backscatter,TanDEM-X,TerraSAR-X,X-band,radar brightness,synthetic aperture radar (SAR)
Radar imaging,Synthetic aperture radar,Remote sensing,Backscatter,Data acquisition,Man-portable radar,Remote sensing application,Digital elevation model,Contextual image classification,Mathematics
Journal
Volume
Issue
ISSN
52
9
0196-2892
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Paola Rizzoli1154.34
Benjamin Bräutigam213321.47