Hierarchical Bayesian Data Analysis in Radiometric SAR System Calibration: A Case Study on Transponder Calibration with RADARSAT-2 Data
| Autor: | Björn J. Döring, Kersten Schmidt, Matthias Jirousek, Daniel Rudolf, Jens Reimann, Sebastian Raab, John Walter Antony, Marco Schwerdt |
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| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: | |
| Zdroj: | Remote Sensing, Vol 5, Iss 12, Pp 6667-6690 (2013) |
| Druh dokumentu: | article |
| ISSN: | 2072-4292 10149228 |
| DOI: | 10.3390/rs5126667 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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