Effect of prior information on Bayesian estimates of dielectric constant from remotely sensed data

Claudia Notarnicola; Francesco Posa

doi:10.1117/12.463064

7 March 2003 Effect of prior information on Bayesian estimates of dielectric constant from remotely sensed data

Claudia Notarnicola, Francesco Posa

Proceedings Volume 4883, SAR Image Analysis, Modeling, and Techniques V; (2003) https://doi.org/10.1117/12.463064
Event: International Symposium on Remote Sensing, 2002, Crete, Greece

Abstract

Bayesian inference has been proved to be a valuable tool in inversion processes. In this study, it is applied in two cases, both aimed at estimating dielectric constant from radar measurements. The first case is devoted to merge point measurements deriving from radiometer and scatterometer data on bare soils. The second case uses, in the inversion process, only active scatterometer data, but introduces supplementary information from the simulations of a hydrological model. In this Bayesian algorithm the key point is the evaluation of a joint probability density function based on the knowledge of data sets consisting of soil parameters measurements and the corresponding remote sensed data. It is obtained by applying the maximum likelihood procedure. As a further step, the influence of prior information about roughness is assessed within the context of the dielectric constant retrieval. At the beginning, a prior uniform distribution is assumed for all surface parameters. Subsequently, a non-uniform prior distribution, based on field measurements, is introduced in order to verify its impact on the estimates and the relative errors.

Citation Download Citation

Claudia Notarnicola and Francesco Posa "Effect of prior information on Bayesian estimates of dielectric constant from remotely sensed data", Proc. SPIE 4883, SAR Image Analysis, Modeling, and Techniques V, (7 March 2003); https://doi.org/10.1117/12.463064

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available