Satellite Image Restoration using the VMCA Model

Autores
Anderson, Sharolyn; Fonstad, Mark Alan; Delrieux, Claudio
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
One of the most common patterns of the geographic landscape is the fractal or nearfractal form. Unfortunately, most traditional methods of spatial interpolation assume some type of continuous and regionalizeable variation of the underlying geographic form, an assumption at odds with the observed fractal properties of many landscapes. An extremely simple iterative algorithm, the voter model cellular automata (CA), produces discontinuous fractal patterns useful for interpolation while at the same preserving a realistic amount of spatial autocorrelation, extracted from neighboring existing data, also found in these landscapes. This adaptive algorithm is based on the principle of iteratively interpolating a missing data point using the value of a randomly selected neighbor cell. The model can also be extended to interpolate field-like variables by adding random deviations from the randomly chosen neighbor cell value. In this paper we explore the effect of satellite image restoration using a simple VMCA over obscured by clouds areas. This model is computationally advantageous, given its localty and restricted underlying computational model. Thus, an adequate computer implementation may perform significantly faster than other restoration methods, with roughly similar overall results. Also the local/scalable/parallelizable nature of CAs allows hardware FPGA implementation that might be embedded within the imager devices in satellites and remote sensors. On the other end, a GPU implementation might take advantage of highly specialized parallel processors capablde of restoring huge images in real time.
Eje: Computación gráfica, visualización e imágenes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Satellite Image Restoration
Visual
VMCA Model
Graphics
Real time
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/20823

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spelling Satellite Image Restoration using the VMCA ModelAnderson, SharolynFonstad, Mark AlanDelrieux, ClaudioCiencias InformáticasSatellite Image RestorationVisualVMCA ModelGraphicsReal timeOne of the most common patterns of the geographic landscape is the fractal or nearfractal form. Unfortunately, most traditional methods of spatial interpolation assume some type of continuous and regionalizeable variation of the underlying geographic form, an assumption at odds with the observed fractal properties of many landscapes. An extremely simple iterative algorithm, the voter model cellular automata (CA), produces discontinuous fractal patterns useful for interpolation while at the same preserving a realistic amount of spatial autocorrelation, extracted from neighboring existing data, also found in these landscapes. This adaptive algorithm is based on the principle of iteratively interpolating a missing data point using the value of a randomly selected neighbor cell. The model can also be extended to interpolate field-like variables by adding random deviations from the randomly chosen neighbor cell value. In this paper we explore the effect of satellite image restoration using a simple VMCA over obscured by clouds areas. This model is computationally advantageous, given its localty and restricted underlying computational model. Thus, an adequate computer implementation may perform significantly faster than other restoration methods, with roughly similar overall results. Also the local/scalable/parallelizable nature of CAs allows hardware FPGA implementation that might be embedded within the imager devices in satellites and remote sensors. On the other end, a GPU implementation might take advantage of highly specialized parallel processors capablde of restoring huge images in real time.Eje: Computación gráfica, visualización e imágenesRed de Universidades con Carreras en Informática (RedUNCI)2006-06info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/20823enginfo:eu-repo/semantics/altIdentifier/isbn/950-9474-35-5info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:54:25Zoai:sedici.unlp.edu.ar:10915/20823Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:25.664SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Satellite Image Restoration using the VMCA Model
title Satellite Image Restoration using the VMCA Model
spellingShingle Satellite Image Restoration using the VMCA Model
Anderson, Sharolyn
Ciencias Informáticas
Satellite Image Restoration
Visual
VMCA Model
Graphics
Real time
title_short Satellite Image Restoration using the VMCA Model
title_full Satellite Image Restoration using the VMCA Model
title_fullStr Satellite Image Restoration using the VMCA Model
title_full_unstemmed Satellite Image Restoration using the VMCA Model
title_sort Satellite Image Restoration using the VMCA Model
dc.creator.none.fl_str_mv Anderson, Sharolyn
Fonstad, Mark Alan
Delrieux, Claudio
author Anderson, Sharolyn
author_facet Anderson, Sharolyn
Fonstad, Mark Alan
Delrieux, Claudio
author_role author
author2 Fonstad, Mark Alan
Delrieux, Claudio
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Satellite Image Restoration
Visual
VMCA Model
Graphics
Real time
topic Ciencias Informáticas
Satellite Image Restoration
Visual
VMCA Model
Graphics
Real time
dc.description.none.fl_txt_mv One of the most common patterns of the geographic landscape is the fractal or nearfractal form. Unfortunately, most traditional methods of spatial interpolation assume some type of continuous and regionalizeable variation of the underlying geographic form, an assumption at odds with the observed fractal properties of many landscapes. An extremely simple iterative algorithm, the voter model cellular automata (CA), produces discontinuous fractal patterns useful for interpolation while at the same preserving a realistic amount of spatial autocorrelation, extracted from neighboring existing data, also found in these landscapes. This adaptive algorithm is based on the principle of iteratively interpolating a missing data point using the value of a randomly selected neighbor cell. The model can also be extended to interpolate field-like variables by adding random deviations from the randomly chosen neighbor cell value. In this paper we explore the effect of satellite image restoration using a simple VMCA over obscured by clouds areas. This model is computationally advantageous, given its localty and restricted underlying computational model. Thus, an adequate computer implementation may perform significantly faster than other restoration methods, with roughly similar overall results. Also the local/scalable/parallelizable nature of CAs allows hardware FPGA implementation that might be embedded within the imager devices in satellites and remote sensors. On the other end, a GPU implementation might take advantage of highly specialized parallel processors capablde of restoring huge images in real time.
Eje: Computación gráfica, visualización e imágenes
Red de Universidades con Carreras en Informática (RedUNCI)
description One of the most common patterns of the geographic landscape is the fractal or nearfractal form. Unfortunately, most traditional methods of spatial interpolation assume some type of continuous and regionalizeable variation of the underlying geographic form, an assumption at odds with the observed fractal properties of many landscapes. An extremely simple iterative algorithm, the voter model cellular automata (CA), produces discontinuous fractal patterns useful for interpolation while at the same preserving a realistic amount of spatial autocorrelation, extracted from neighboring existing data, also found in these landscapes. This adaptive algorithm is based on the principle of iteratively interpolating a missing data point using the value of a randomly selected neighbor cell. The model can also be extended to interpolate field-like variables by adding random deviations from the randomly chosen neighbor cell value. In this paper we explore the effect of satellite image restoration using a simple VMCA over obscured by clouds areas. This model is computationally advantageous, given its localty and restricted underlying computational model. Thus, an adequate computer implementation may perform significantly faster than other restoration methods, with roughly similar overall results. Also the local/scalable/parallelizable nature of CAs allows hardware FPGA implementation that might be embedded within the imager devices in satellites and remote sensors. On the other end, a GPU implementation might take advantage of highly specialized parallel processors capablde of restoring huge images in real time.
publishDate 2006
dc.date.none.fl_str_mv 2006-06
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
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language eng
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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