Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintética
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/28999 |
Resumo: | The study and correct interpretation of Synthetic Aperture Radar (SAR) images are relevant in the context of remote sensing and can be of great value to civil and military interests. This thesis addresses aspects of modeling and monitoring SAR images. More specifically, into three independent self-contained articles, new statistical models for modeling SAR images are introduced, as well as tools for detecting anomalies, based on probabilistic models. The first article introduces the concept of control charts for detecting anomalies in digital images. It is based on a reparameterization of the Burr XII distribution. This distribution has, as particular cases, usual models for SAR images in the intensity and amplitude format: the single-look G 0 I and single-look G 0 A distributions. Several properties and statistical measures useful for describing SAR images are presented. Through numerical studies on simulated and real images, it is shown that the proposed control chart is potentially useful for detecting anomalies in SAR images. More importantly, it presents the lowest occurrence of false alarms compared to analogous applications of other distributions in the context of SAR. Articles 2 and 3 introduce new probability distributions for modeling SAR images. The area of knowledge related to proposing probability models has been extensively investigated. However, to the best of our knowledge, it is not fully explored in the context of SAR images. Article 2 introduces the exponentiated transmuted-inverted beta distribution (ET-IB). It is a generalization of the inverted beta distribution, an important texture model among the well-known multiplicative models for SAR images. Properties are presented, such as measures based on the quantiles of the distribution: median, skewness, and kurtosis coefficients. Next, simulation studies and applications to real data are carried out, showing the potential of the ET-IB distribution for modeling amplitude-format images from forest and ocean regions. In article 3 an approximation to the G 0 A distribution, called distribution LA, is proposed. The G 0 A distribution is known as the universal model for modeling SAR images in amplitude format. However, it has some analytical limitations. Our proposal is an analytically more tractable alternative since it does not present any special function in its probability density, cumulative distribution, and quantile functions. This tractability is useful for proposing remote sensing tools based on distribution quantiles, as well as for real-time applications. Several useful measures are calculated and presented, all without analytical limitations, for describing SAR images related to central tendency, variability, and density shape. Still, such measures’ behavior is verified in the face of the variation of the parameters that index the distribution. Finally, numerical studies are conducted to compare the performance of the G 0 A and LA distributions. Images referring to regions of forest, ocean, urban, industrial, vegetation, and railways are considered. In general, the results show a better performance of the LA distribution. It is expected that the contributions of this thesis can be helpful in future studies and applications related to the context of remote sensing. |
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Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintéticaNew statistical models for processing and monitoring synthetic aperture radar imagesDetecção de anomaliasDistribuição de probabilidadeGráfico de controleObservação da TerraAnomaly detectionControl chartEarth observationProbability distributionCNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAOThe study and correct interpretation of Synthetic Aperture Radar (SAR) images are relevant in the context of remote sensing and can be of great value to civil and military interests. This thesis addresses aspects of modeling and monitoring SAR images. More specifically, into three independent self-contained articles, new statistical models for modeling SAR images are introduced, as well as tools for detecting anomalies, based on probabilistic models. The first article introduces the concept of control charts for detecting anomalies in digital images. It is based on a reparameterization of the Burr XII distribution. This distribution has, as particular cases, usual models for SAR images in the intensity and amplitude format: the single-look G 0 I and single-look G 0 A distributions. Several properties and statistical measures useful for describing SAR images are presented. Through numerical studies on simulated and real images, it is shown that the proposed control chart is potentially useful for detecting anomalies in SAR images. More importantly, it presents the lowest occurrence of false alarms compared to analogous applications of other distributions in the context of SAR. Articles 2 and 3 introduce new probability distributions for modeling SAR images. The area of knowledge related to proposing probability models has been extensively investigated. However, to the best of our knowledge, it is not fully explored in the context of SAR images. Article 2 introduces the exponentiated transmuted-inverted beta distribution (ET-IB). It is a generalization of the inverted beta distribution, an important texture model among the well-known multiplicative models for SAR images. Properties are presented, such as measures based on the quantiles of the distribution: median, skewness, and kurtosis coefficients. Next, simulation studies and applications to real data are carried out, showing the potential of the ET-IB distribution for modeling amplitude-format images from forest and ocean regions. In article 3 an approximation to the G 0 A distribution, called distribution LA, is proposed. The G 0 A distribution is known as the universal model for modeling SAR images in amplitude format. However, it has some analytical limitations. Our proposal is an analytically more tractable alternative since it does not present any special function in its probability density, cumulative distribution, and quantile functions. This tractability is useful for proposing remote sensing tools based on distribution quantiles, as well as for real-time applications. Several useful measures are calculated and presented, all without analytical limitations, for describing SAR images related to central tendency, variability, and density shape. Still, such measures’ behavior is verified in the face of the variation of the parameters that index the distribution. Finally, numerical studies are conducted to compare the performance of the G 0 A and LA distributions. Images referring to regions of forest, ocean, urban, industrial, vegetation, and railways are considered. In general, the results show a better performance of the LA distribution. It is expected that the contributions of this thesis can be helpful in future studies and applications related to the context of remote sensing.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESO estudo e a correta interpretação de imagens de radar de abertura sintética (SAR, do inglês Synthetic Aperture Radar) possuem relevância no contexto de sensoriamento remoto e podem ser de grande valor para interesses civis e militares. Esta tese aborda aspectos sobre modelagem e monitoramento de imagens SAR. Mais especificamente, em três artigos autocontidos independentes, são introduzidos novos modelos estatísticos para modelagem de imagens SAR, assim como ferramentas para detecção de anomalias, baseadas em modelos probabilísticos. O primeiro artigo introduz o conceito de gráficos de controle para detecção de anomalias em imagens digitais. Ele é baseado em uma proposta de reparametrização da distribuição Burr XII. Essa distribuição tem, como casos particulares, importantes modelos usuais para imagens SAR no formato de intensidade e amplitude: as distribuições single-look G 0 I e single-look G 0 A . São apresentadas diversas propriedades e medidas estatísticas úteis para descrição de imagens SAR. Por meio de estudos numéricos em imagens simuladas e reais, é mostrado que o gráfico de controle proposto é potencialmente útil para detectar anomalias em imagens SAR e, mais importante, apresenta a menor ocorrência de falsos alvos detectados, quando comparado a aplicações análogas de outras distribuições usuais no contexto de SAR. Nos artigos 2 e 3, são introduzidas novas distribuições de probabilidade para modelagem de imagens SAR. A área do conhecimento relativa à proposição de modelos de probabilidade tem sido bastante investigada. Entretanto, ao melhor de nosso conhecimento, a mesma não é totalmente explorada no contexto de imagens SAR. O artigo 2 introduz a distribuição beta invertida transmutada exponencializada (ET-IB). Trata-se de uma generalização da distribuição beta invertida, um importante modelo de textura em modelos multiplicativos para imagens SAR. São apresentadas propriedades com base nos quantis da distribuição: mediana e coeficientes de assimetria e curtose. Na sequência, estudos de simulação e aplicações a dados reais são realizados, mostrando o potencial da distribuição ET-IB para modelagem de imagens em formato de amplitude provenientes de regiões de floresta e oceano. No artigo 3 uma aproximação à distribuição G 0 A , denominada distribuição LA, é proposta. A distribuição G 0 A é conhecida como modelo universal para modelagem de imagens SAR em formato de amplitude. Entretanto, possui algumas limitações analíticas. O modelo LA é uma alternativa analiticamente mais tratável, uma vez que não apresenta nenhuma função especial em sua função densidade de probabilidade, função distribuição acumulada e função quantílica. Isso é útil para a proposta de ferramentas de sensoriamento remoto baseadas nos quantis da distribuição, assim como para aplicações em tempo real. São calculadas e apresentadas diversas medidas úteis, todas sem limitações analíticas, para descrição de imagens SAR, relacionadas a tendência central, variabilidade e forma da densidade. Ainda, é verificado o comportamento de tais medidas diante da variação dos parâmetros que indexam a distribuição.Por fim, estudos numéricos são conduzidos visando comparar o desempenho das distribuições G 0 A e LA. São consideradas imagens simuladas e reais referentes a regiões de floresta, oceano, urbana, industrias, vegetação e ferrovias. Os resultados mostram um melhor desempenho da distribuição LA. Espera-se que as contribuições desta tese possam auxiliar futuros estudos e aplicações relacionadas ao contexto de sensoriamento remoto.Universidade Federal de Santa MariaBrasilEngenharia de ProduçãoUFSMPrograma de Pós-Graduação em Engenharia de ProduçãoCentro de TecnologiaBayer, Fabio Marianohttp://lattes.cnpq.br/9904863693302949Guerra, Renata RojasRamirez, Fernando Arturo PeñaPalm, Bruna GregoryFrery, AlejandroSilva, Paulo Henrique Ferreira daPereira, Murilo Sagrillo2023-05-08T14:15:26Z2023-05-08T14:15:26Z2023-03-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/28999porAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2023-05-08T14:15:26Zoai:repositorio.ufsm.br:1/28999Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2023-05-08T14:15:26Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintética New statistical models for processing and monitoring synthetic aperture radar images |
title |
Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintética |
spellingShingle |
Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintética Pereira, Murilo Sagrillo Detecção de anomalias Distribuição de probabilidade Gráfico de controle Observação da Terra Anomaly detection Control chart Earth observation Probability distribution CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO |
title_short |
Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintética |
title_full |
Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintética |
title_fullStr |
Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintética |
title_full_unstemmed |
Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintética |
title_sort |
Novos modelos estatísticos para processamento e monitoramento de imagens de radar de abertura sintética |
author |
Pereira, Murilo Sagrillo |
author_facet |
Pereira, Murilo Sagrillo |
author_role |
author |
dc.contributor.none.fl_str_mv |
Bayer, Fabio Mariano http://lattes.cnpq.br/9904863693302949 Guerra, Renata Rojas Ramirez, Fernando Arturo Peña Palm, Bruna Gregory Frery, Alejandro Silva, Paulo Henrique Ferreira da |
dc.contributor.author.fl_str_mv |
Pereira, Murilo Sagrillo |
dc.subject.por.fl_str_mv |
Detecção de anomalias Distribuição de probabilidade Gráfico de controle Observação da Terra Anomaly detection Control chart Earth observation Probability distribution CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO |
topic |
Detecção de anomalias Distribuição de probabilidade Gráfico de controle Observação da Terra Anomaly detection Control chart Earth observation Probability distribution CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO |
description |
The study and correct interpretation of Synthetic Aperture Radar (SAR) images are relevant in the context of remote sensing and can be of great value to civil and military interests. This thesis addresses aspects of modeling and monitoring SAR images. More specifically, into three independent self-contained articles, new statistical models for modeling SAR images are introduced, as well as tools for detecting anomalies, based on probabilistic models. The first article introduces the concept of control charts for detecting anomalies in digital images. It is based on a reparameterization of the Burr XII distribution. This distribution has, as particular cases, usual models for SAR images in the intensity and amplitude format: the single-look G 0 I and single-look G 0 A distributions. Several properties and statistical measures useful for describing SAR images are presented. Through numerical studies on simulated and real images, it is shown that the proposed control chart is potentially useful for detecting anomalies in SAR images. More importantly, it presents the lowest occurrence of false alarms compared to analogous applications of other distributions in the context of SAR. Articles 2 and 3 introduce new probability distributions for modeling SAR images. The area of knowledge related to proposing probability models has been extensively investigated. However, to the best of our knowledge, it is not fully explored in the context of SAR images. Article 2 introduces the exponentiated transmuted-inverted beta distribution (ET-IB). It is a generalization of the inverted beta distribution, an important texture model among the well-known multiplicative models for SAR images. Properties are presented, such as measures based on the quantiles of the distribution: median, skewness, and kurtosis coefficients. Next, simulation studies and applications to real data are carried out, showing the potential of the ET-IB distribution for modeling amplitude-format images from forest and ocean regions. In article 3 an approximation to the G 0 A distribution, called distribution LA, is proposed. The G 0 A distribution is known as the universal model for modeling SAR images in amplitude format. However, it has some analytical limitations. Our proposal is an analytically more tractable alternative since it does not present any special function in its probability density, cumulative distribution, and quantile functions. This tractability is useful for proposing remote sensing tools based on distribution quantiles, as well as for real-time applications. Several useful measures are calculated and presented, all without analytical limitations, for describing SAR images related to central tendency, variability, and density shape. Still, such measures’ behavior is verified in the face of the variation of the parameters that index the distribution. Finally, numerical studies are conducted to compare the performance of the G 0 A and LA distributions. Images referring to regions of forest, ocean, urban, industrial, vegetation, and railways are considered. In general, the results show a better performance of the LA distribution. It is expected that the contributions of this thesis can be helpful in future studies and applications related to the context of remote sensing. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-08T14:15:26Z 2023-05-08T14:15:26Z 2023-03-07 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
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publishedVersion |
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http://repositorio.ufsm.br/handle/1/28999 |
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http://repositorio.ufsm.br/handle/1/28999 |
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por |
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por |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
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application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Engenharia de Produção UFSM Programa de Pós-Graduação em Engenharia de Produção Centro de Tecnologia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Engenharia de Produção UFSM Programa de Pós-Graduação em Engenharia de Produção Centro de Tecnologia |
dc.source.none.fl_str_mv |
reponame:Manancial - Repositório Digital da UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
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Universidade Federal de Santa Maria (UFSM) |
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UFSM |
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UFSM |
reponame_str |
Manancial - Repositório Digital da UFSM |
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Manancial - Repositório Digital da UFSM |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
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atendimento.sib@ufsm.br||tedebc@gmail.com |
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