Hierarchical Bayesian Data Analysis in Radiometric SAR System Calibration: A Case Study on Transponder Calibration with RADARSAT-2 Data

Remote Sensing

Björn J. Döring¹  Kersten Schmidt²  Matthias Jirousek²  Daniel Rudolf²  Jens Reimann²  Sebastian Raab²  John Walter Antony² 

[1] Microwaves and Radar Institute, German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Weßling, Germany;

关键词: synthetic aperture radar;    radiometric calibration;    external calibration;    Bayesian data analysis;    transponder;   

DOI  :  10.3390/rs5126667

来源: mdpi

【 摘 要 】

A synthetic aperture radar (SAR) system requires external absolute calibration so that radiometric measurements can be exploited in numerous scientific and commercial applications. Besides estimating a calibration factor, metrological standards also demand the derivation of a respective calibration uncertainty. This uncertainty is currently not systematically determined. Here for the first time it is proposed to use hierarchical modeling and Bayesian statistics as a consistent method for handling and analyzing the hierarchical data typically acquired during external calibration campaigns. Through the use of Markov chain Monte Carlo simulations, a joint posterior probability can be conveniently derived from measurement data despite the necessary grouping of data samples. The applicability of the method is demonstrated through a case study: The radar reflectivity of DLR’s new C-band Kalibri transponder is derived through a series of RADARSAT-2 acquisitions and a comparison with reference point targets (corner reflectors). The systematic derivation of calibration uncertainties is seen as an important step toward traceable radiometric calibration of synthetic aperture radars.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland