A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements

When a passive microwave footprint intersects objects on the ground with different spectral characteristics, the corresponding observation is mixed. The retrieval of geophysical parameters is limited by this mixture. We propose to partition the study region into objects following an object-based ima...

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Detalles Bibliográficos
Publicado: 2019
Materias:
Expectation-maximization (EM) algorithms
inverse problems
passive microwave remote sensing
Cells
Cytology
Image segmentation
Iterative methods
Maximum principle
Remote sensing
Brightness temperatures
Expectation-maximization algorithms
Geophysical parameters
Object based image analysis
Passive microwave data
Passive microwave remote sensing
Passive microwaves
Spectral characteristics
Inverse problems
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01962892_v57_n3_p1347_Grimson
http://hdl.handle.net/20.500.12110/paper_01962892_v57_n3_p1347_Grimson
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_01962892_v57_n3_p1347_Grimson
record_format dspace
spelling paper:paper_01962892_v57_n3_p1347_Grimson2023-06-08T15:20:28Z A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements Expectation-maximization (EM) algorithms inverse problems passive microwave remote sensing Cells Cytology Image segmentation Iterative methods Maximum principle Remote sensing Brightness temperatures Expectation-maximization algorithms Geophysical parameters Object based image analysis Passive microwave data Passive microwave remote sensing Passive microwaves Spectral characteristics Inverse problems When a passive microwave footprint intersects objects on the ground with different spectral characteristics, the corresponding observation is mixed. The retrieval of geophysical parameters is limited by this mixture. We propose to partition the study region into objects following an object-based image analysis procedure and then to refine this partition into small cells. Then, we introduce a statistical method to estimate the brightness temperature (TB) of each cell. The method assumes that TB of the cells corresponding to the same object is identically distributed and that the TB heterogeneity within each cell can be neglected. The implementation is based on an iterative expectation-maximization algorithm. We evaluated the proposed method using synthetic images and applied it to grid the TBs of sample AMSR -2 real data over a coastal region in Argentina. © 1980-2012 IEEE. 2019 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01962892_v57_n3_p1347_Grimson http://hdl.handle.net/20.500.12110/paper_01962892_v57_n3_p1347_Grimson
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Expectation-maximization (EM) algorithms
inverse problems
passive microwave remote sensing
Cells
Cytology
Image segmentation
Iterative methods
Maximum principle
Remote sensing
Brightness temperatures
Expectation-maximization algorithms
Geophysical parameters
Object based image analysis
Passive microwave data
Passive microwave remote sensing
Passive microwaves
Spectral characteristics
Inverse problems
spellingShingle Expectation-maximization (EM) algorithms
inverse problems
passive microwave remote sensing
Cells
Cytology
Image segmentation
Iterative methods
Maximum principle
Remote sensing
Brightness temperatures
Expectation-maximization algorithms
Geophysical parameters
Object based image analysis
Passive microwave data
Passive microwave remote sensing
Passive microwaves
Spectral characteristics
Inverse problems
A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements
topic_facet Expectation-maximization (EM) algorithms
inverse problems
passive microwave remote sensing
Cells
Cytology
Image segmentation
Iterative methods
Maximum principle
Remote sensing
Brightness temperatures
Expectation-maximization algorithms
Geophysical parameters
Object based image analysis
Passive microwave data
Passive microwave remote sensing
Passive microwaves
Spectral characteristics
Inverse problems
description When a passive microwave footprint intersects objects on the ground with different spectral characteristics, the corresponding observation is mixed. The retrieval of geophysical parameters is limited by this mixture. We propose to partition the study region into objects following an object-based image analysis procedure and then to refine this partition into small cells. Then, we introduce a statistical method to estimate the brightness temperature (TB) of each cell. The method assumes that TB of the cells corresponding to the same object is identically distributed and that the TB heterogeneity within each cell can be neglected. The implementation is based on an iterative expectation-maximization algorithm. We evaluated the proposed method using synthetic images and applied it to grid the TBs of sample AMSR -2 real data over a coastal region in Argentina. © 1980-2012 IEEE.
title A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements
title_short A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements
title_full A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements
title_fullStr A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements
title_full_unstemmed A Statistical Inverse Method for Gridding Passive Microwave Data with Mixed Measurements
title_sort statistical inverse method for gridding passive microwave data with mixed measurements
publishDate 2019
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01962892_v57_n3_p1347_Grimson
http://hdl.handle.net/20.500.12110/paper_01962892_v57_n3_p1347_Grimson
_version_ 1768544592014082048

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