Segmentación de coberturas de la tierra espectralmente similares empleando campos aleatorios de Markov y características de textura estructural y estocástica
In this thesis,Markovian modeling is applied to perform segmentation of land cover from remote sensing and digital images. The segmentation problem is approached as a classification problem, where the goal is to decompose an image in a set of homogeneous regions using a similarity characteristics se...
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| Format: | doctoral thesis |
| Status: | Versión aceptada para publicación |
| Publication Date: | 2009 |
| Country: | México |
| Institution: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repository: | Repositorio Institucional del INAOE |
| Language: | Spanish |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/399 |
| Online Access: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/399 |
| Access Level: | Open access |
| Keyword: | info:eu-repo/classification/Segmentación de imagen/Image segmentation info:eu-repo/classification/Textura de imagen/Image texture info:eu-repo/classification/Procesos de Markov/Markov processes info:eu-repo/classification/Remote sensing/Remote sensing info:eu-repo/classification/Análisis espectral/Spectral analysis info:eu-repo/classification/Procesos estocásticos/Stochastic processes info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Summary: | In this thesis,Markovian modeling is applied to perform segmentation of land cover from remote sensing and digital images. The segmentation problem is approached as a classification problem, where the goal is to decompose an image in a set of homogeneous regions using a similarity characteristics set. In the Bayesian framework using Markov Random Fields (MRF) the image texture is introduced as clique potentials of a second-order posterior energy function. These clique potentials or texture fields are obtained by means of the 2-DWold decomposition and the obtained final function is called texture energy function (TEF). Texture fields are obtained from the frequency domain, therefore, a model is defined through both the spatial (contextual constraint) and frequency (reference fields) domain. This model allows us to define better the segmented image borders. Experiments were carried out on a variety of synthetic and real images. From the segmentation results, it is observed that by incorporating texture fields to the posterior energy function, the segmentation quality is improved. In this thesis, the main result is the TEF function which is possible to introduce within MRF and tree-structured Markov random fields (TS-MRF) models. In this way, a new model for segmentation of classes with similar spectral response based on TSMRF and the TEF function is proposed. In addition, a methodology that involves the TEF function and a stochastic geometry model to improve image segmentation is proposed. The segmentation preliminary results on synthetic images are encouraging, but there is still work to be done in this direction. |
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