Probabilistic surface change detection and measurement from digital aerial stereo images.

Detalhes bibliográficos
Autor(a) principal: Jalobeanu, André
Data de Publicação: 2010
Outros Autores: Gama, Cristina, Gonçalves, José
Tipo de documento: Artigo de conferência
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/5404
Resumo: We propose a new method to measure changes in terrain topography from two optical stereo image pairs acquired at different dates. The main novelty is in the ability of computing the spatial distribution of uncertainty, thanks to stochastic modeling and probabilistic inference. Thus, scientists will have access to quantitative error estimates of local surface variation, so they can check the statistical significance of elevation changes, and make, where changes have occurred, consistent measurements of volume or shape evolution. The main application area is geomorphology, as the method can help study phenomena such as coastal cliff erosion, sand dune displacement and various transport mechanisms through the computation of volume changes. It can also help measure vegetation growth, and virtually any kind of evolution of the surface. We first start by inferring a dense disparity map from two images, assuming a known viewing geometry. The images are accurately rectified in order to constrain the deformation on one of the axes, so we only have to infer a one-dimensional parameter field. The probabilistic approach provides a rigorous framework for parameter estimation and error computation, so all the disparities are described as random variables. We define a generative model for both images given all model variables. It mainly consists of warping the scene using B-Splines, and defining a spatially adaptive stochastic model of the radiometric differences between the two views. The inversion, which is an ill-posed inverse problem, requires regularization, achieved through a smoothness prior model. Bayesian inference allows us to recover disparities as probability distributions. This is done on each stereo pair, then disparity maps are transformed into surface models in a common ground frame in order to perform the comparison. We apply this technique to high resolution digital aerial images of the Portuguese coast to detect cliff erosion and quantify the effects of weathering.
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spelling Probabilistic surface change detection and measurement from digital aerial stereo images.probabilistic inferencequantitative error estimatesBayesian inferenceterrain topographyWe propose a new method to measure changes in terrain topography from two optical stereo image pairs acquired at different dates. The main novelty is in the ability of computing the spatial distribution of uncertainty, thanks to stochastic modeling and probabilistic inference. Thus, scientists will have access to quantitative error estimates of local surface variation, so they can check the statistical significance of elevation changes, and make, where changes have occurred, consistent measurements of volume or shape evolution. The main application area is geomorphology, as the method can help study phenomena such as coastal cliff erosion, sand dune displacement and various transport mechanisms through the computation of volume changes. It can also help measure vegetation growth, and virtually any kind of evolution of the surface. We first start by inferring a dense disparity map from two images, assuming a known viewing geometry. The images are accurately rectified in order to constrain the deformation on one of the axes, so we only have to infer a one-dimensional parameter field. The probabilistic approach provides a rigorous framework for parameter estimation and error computation, so all the disparities are described as random variables. We define a generative model for both images given all model variables. It mainly consists of warping the scene using B-Splines, and defining a spatially adaptive stochastic model of the radiometric differences between the two views. The inversion, which is an ill-posed inverse problem, requires regularization, achieved through a smoothness prior model. Bayesian inference allows us to recover disparities as probability distributions. This is done on each stereo pair, then disparity maps are transformed into surface models in a common ground frame in order to perform the comparison. We apply this technique to high resolution digital aerial images of the Portuguese coast to detect cliff erosion and quantify the effects of weathering.IEEE Geoscience and Remote Sensing Society and the IGARSS2012-11-08T17:47:52Z2012-11-082010-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://hdl.handle.net/10174/5404http://hdl.handle.net/10174/5404porJalobeanu, A., Gama, C., Gonçalves, J. (2010). Probabilistic surface change detection and measurement from digital aerial stereo images. IEEE Geoscience and Remote Sensing Society and the IGARSS 2010. 25-30 Julho, Honolulu, Havai, USA.simnaonaondcgama@uevora.ptnd250Jalobeanu, AndréGama, CristinaGonçalves, Joséinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T18:44:03Zoai:dspace.uevora.pt:10174/5404Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:00:22.441680Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Probabilistic surface change detection and measurement from digital aerial stereo images.
title Probabilistic surface change detection and measurement from digital aerial stereo images.
spellingShingle Probabilistic surface change detection and measurement from digital aerial stereo images.
Jalobeanu, André
probabilistic inference
quantitative error estimates
Bayesian inference
terrain topography
title_short Probabilistic surface change detection and measurement from digital aerial stereo images.
title_full Probabilistic surface change detection and measurement from digital aerial stereo images.
title_fullStr Probabilistic surface change detection and measurement from digital aerial stereo images.
title_full_unstemmed Probabilistic surface change detection and measurement from digital aerial stereo images.
title_sort Probabilistic surface change detection and measurement from digital aerial stereo images.
author Jalobeanu, André
author_facet Jalobeanu, André
Gama, Cristina
Gonçalves, José
author_role author
author2 Gama, Cristina
Gonçalves, José
author2_role author
author
dc.contributor.author.fl_str_mv Jalobeanu, André
Gama, Cristina
Gonçalves, José
dc.subject.por.fl_str_mv probabilistic inference
quantitative error estimates
Bayesian inference
terrain topography
topic probabilistic inference
quantitative error estimates
Bayesian inference
terrain topography
description We propose a new method to measure changes in terrain topography from two optical stereo image pairs acquired at different dates. The main novelty is in the ability of computing the spatial distribution of uncertainty, thanks to stochastic modeling and probabilistic inference. Thus, scientists will have access to quantitative error estimates of local surface variation, so they can check the statistical significance of elevation changes, and make, where changes have occurred, consistent measurements of volume or shape evolution. The main application area is geomorphology, as the method can help study phenomena such as coastal cliff erosion, sand dune displacement and various transport mechanisms through the computation of volume changes. It can also help measure vegetation growth, and virtually any kind of evolution of the surface. We first start by inferring a dense disparity map from two images, assuming a known viewing geometry. The images are accurately rectified in order to constrain the deformation on one of the axes, so we only have to infer a one-dimensional parameter field. The probabilistic approach provides a rigorous framework for parameter estimation and error computation, so all the disparities are described as random variables. We define a generative model for both images given all model variables. It mainly consists of warping the scene using B-Splines, and defining a spatially adaptive stochastic model of the radiometric differences between the two views. The inversion, which is an ill-posed inverse problem, requires regularization, achieved through a smoothness prior model. Bayesian inference allows us to recover disparities as probability distributions. This is done on each stereo pair, then disparity maps are transformed into surface models in a common ground frame in order to perform the comparison. We apply this technique to high resolution digital aerial images of the Portuguese coast to detect cliff erosion and quantify the effects of weathering.
publishDate 2010
dc.date.none.fl_str_mv 2010-07-01T00:00:00Z
2012-11-08T17:47:52Z
2012-11-08
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dc.relation.none.fl_str_mv Jalobeanu, A., Gama, C., Gonçalves, J. (2010). Probabilistic surface change detection and measurement from digital aerial stereo images. IEEE Geoscience and Remote Sensing Society and the IGARSS 2010. 25-30 Julho, Honolulu, Havai, USA.
sim
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cgama@uevora.pt
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dc.publisher.none.fl_str_mv IEEE Geoscience and Remote Sensing Society and the IGARSS
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