Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data

Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Barber, M., Grings, F., Perna, P., Piscitelli, M., Maas, M., Bruscantini, C., Jacobo-Berlles, J., Karszenbaum, H.
Formato:	JOUR
Materias:	Bayesian methods inverse problems radar applications soil moisture synthetic aperture radar Backscattering coefficients Bayesian approaches Bayesian estimators Bayesian model Bayesian retrieval Error sources Mathematical modeling Multilooking Optimum parameters Polarimetric SAR Prior information Radar applications SAR data SAR Images Scattering model Soil conditions Soil heterogeneity Soil moisture retrievals Soil parameters Spatial heterogeneity Speckle effects Speckle noise Statistical distribution Algorithms Bayesian networks Computer simulation Estimation Inverse problems Polarimeters Soil moisture Speckle Synthetic aperture radar Uncertainty analysis Geologic models accuracy assessment algorithm backscatter Bayesian analysis error analysis heterogeneity inverse problem noise numerical model statistical distribution
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_19391404_v5_n3_p942_Barber
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_19391404_v5_n3_p942_Barber
record_format	dspace
spelling	todo:paper_19391404_v5_n3_p942_Barber2023-10-03T16:36:45Z Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data Barber, M. Grings, F. Perna, P. Piscitelli, M. Maas, M. Bruscantini, C. Jacobo-Berlles, J. Karszenbaum, H. Bayesian methods inverse problems radar applications soil moisture synthetic aperture radar Backscattering coefficients Bayesian approaches Bayesian estimators Bayesian methods Bayesian model Bayesian retrieval Error sources Mathematical modeling Multilooking Optimum parameters Polarimetric SAR Prior information Radar applications SAR data SAR Images Scattering model Soil conditions Soil heterogeneity Soil moisture retrievals Soil parameters Spatial heterogeneity Speckle effects Speckle noise Statistical distribution Algorithms Bayesian networks Computer simulation Estimation Inverse problems Polarimeters Soil moisture Speckle Synthetic aperture radar Uncertainty analysis Geologic models accuracy assessment algorithm backscatter Bayesian analysis error analysis heterogeneity inverse problem noise numerical model soil moisture statistical distribution synthetic aperture radar Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh's model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimization-based procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition. © 2012 IEEE. Fil:Barber, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Grings, F. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Perna, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Bruscantini, C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Jacobo-Berlles, J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Karszenbaum, H. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_19391404_v5_n3_p942_Barber
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Bayesian methods inverse problems radar applications soil moisture synthetic aperture radar Backscattering coefficients Bayesian approaches Bayesian estimators Bayesian methods Bayesian model Bayesian retrieval Error sources Mathematical modeling Multilooking Optimum parameters Polarimetric SAR Prior information Radar applications SAR data SAR Images Scattering model Soil conditions Soil heterogeneity Soil moisture retrievals Soil parameters Spatial heterogeneity Speckle effects Speckle noise Statistical distribution Algorithms Bayesian networks Computer simulation Estimation Inverse problems Polarimeters Soil moisture Speckle Synthetic aperture radar Uncertainty analysis Geologic models accuracy assessment algorithm backscatter Bayesian analysis error analysis heterogeneity inverse problem noise numerical model soil moisture statistical distribution synthetic aperture radar
spellingShingle	Bayesian methods inverse problems radar applications soil moisture synthetic aperture radar Backscattering coefficients Bayesian approaches Bayesian estimators Bayesian methods Bayesian model Bayesian retrieval Error sources Mathematical modeling Multilooking Optimum parameters Polarimetric SAR Prior information Radar applications SAR data SAR Images Scattering model Soil conditions Soil heterogeneity Soil moisture retrievals Soil parameters Spatial heterogeneity Speckle effects Speckle noise Statistical distribution Algorithms Bayesian networks Computer simulation Estimation Inverse problems Polarimeters Soil moisture Speckle Synthetic aperture radar Uncertainty analysis Geologic models accuracy assessment algorithm backscatter Bayesian analysis error analysis heterogeneity inverse problem noise numerical model soil moisture statistical distribution synthetic aperture radar Barber, M. Grings, F. Perna, P. Piscitelli, M. Maas, M. Bruscantini, C. Jacobo-Berlles, J. Karszenbaum, H. Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data
topic_facet	Bayesian methods inverse problems radar applications soil moisture synthetic aperture radar Backscattering coefficients Bayesian approaches Bayesian estimators Bayesian methods Bayesian model Bayesian retrieval Error sources Mathematical modeling Multilooking Optimum parameters Polarimetric SAR Prior information Radar applications SAR data SAR Images Scattering model Soil conditions Soil heterogeneity Soil moisture retrievals Soil parameters Spatial heterogeneity Speckle effects Speckle noise Statistical distribution Algorithms Bayesian networks Computer simulation Estimation Inverse problems Polarimeters Soil moisture Speckle Synthetic aperture radar Uncertainty analysis Geologic models accuracy assessment algorithm backscatter Bayesian analysis error analysis heterogeneity inverse problem noise numerical model soil moisture statistical distribution synthetic aperture radar
description	Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a soil moisture Bayesian estimator from polarimetric SAR images is proposed to address these issues. This estimator is based on a set of statistical distributions derived for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included, enhancing the performance of the retrieval. The Oh's model is used as scattering model, although it presents a limiting range of validity for the retrieval of soil moisture. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval is investigated. Then, it is compared with the Bayesian retrieval scheme. The results indicate that the Bayesian model enlarges the validity region of the minimization-based procedure. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approaches the minimization-based retrieval. On the other hand, when speckle effects are large, an improvement in the accuracy of the retrieval is achieved by using a precise prior. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multilooking process and prior information required to perform a precise retrieval in a given soil condition. © 2012 IEEE.
format	JOUR
author	Barber, M. Grings, F. Perna, P. Piscitelli, M. Maas, M. Bruscantini, C. Jacobo-Berlles, J. Karszenbaum, H.
author_facet	Barber, M. Grings, F. Perna, P. Piscitelli, M. Maas, M. Bruscantini, C. Jacobo-Berlles, J. Karszenbaum, H.
author_sort	Barber, M.
title	Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data
title_short	Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data
title_full	Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data
title_fullStr	Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data
title_full_unstemmed	Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data
title_sort	speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for sar data
url	http://hdl.handle.net/20.500.12110/paper_19391404_v5_n3_p942_Barber
work_keys_str_mv	AT barberm specklenoiseandsoilheterogeneitiesaserrorsourcesinabayesiansoilmoistureretrievalschemeforsardata AT gringsf specklenoiseandsoilheterogeneitiesaserrorsourcesinabayesiansoilmoistureretrievalschemeforsardata AT pernap specklenoiseandsoilheterogeneitiesaserrorsourcesinabayesiansoilmoistureretrievalschemeforsardata AT piscitellim specklenoiseandsoilheterogeneitiesaserrorsourcesinabayesiansoilmoistureretrievalschemeforsardata AT maasm specklenoiseandsoilheterogeneitiesaserrorsourcesinabayesiansoilmoistureretrievalschemeforsardata AT bruscantinic specklenoiseandsoilheterogeneitiesaserrorsourcesinabayesiansoilmoistureretrievalschemeforsardata AT jacoboberllesj specklenoiseandsoilheterogeneitiesaserrorsourcesinabayesiansoilmoistureretrievalschemeforsardata AT karszenbaumh specklenoiseandsoilheterogeneitiesaserrorsourcesinabayesiansoilmoistureretrievalschemeforsardata
_version_	1807320151898456064

Speckle noise and soil heterogeneities as error sources in a bayesian soil moisture retrieval scheme for SAR data

Ejemplares similares