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Previous issue date: 2006-07-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Oil spill on the sea, accidental or not, generates enormous negative consequences for the affected area. The damages are ambient and economic, mainly with the proximity of these spots of preservation areas and/or coastal zones. The development of automatic techniques for identification of oil spots on the sea surface, captured through Radar images, assist in a complete monitoring of the oceans and seas. However spots of different origins can be visualized in this type of imaging, which is a very difficult task. The system proposed in this work, based on techniques of digital image processing and artificial neural network, has the objective to identify the analyzed spot and to discern between oil and other generating phenomena of spot. Tests in functional blocks that compose the proposed system allow the implementation of different algorithms, as well as its detailed and prompt analysis. The algorithms of digital image processing (speckle filtering and gradient), as well as classifier algorithms (Multilayer Perceptron, Radial Basis Function, Support Vector Machine and Committe Machine) are presented and commented.The final performance of the system, with different kind of classifiers, is presented by ROC curve. The true positive rates are considered agreed with the literature about oil slick detection through SAR images presents / Derramamentos de ?leo sobre o mar, mesmo que acidentais, geram enormes conseq??ncias negativas para a ?rea afetada. Os preju?zos s?o ambientais e econ?micos, principalmente com a proximidade dessas manchas de ?reas de preserva??o e/ou zonas costeiras. O desenvolvimento de t?cnicas autom?ticas para a identifica??o de manchas de ?leo sobre a superf?cie marinha, capturadas atrav?s de imagens de Radar, auxiliam num completo monitoramento dos oceanos e mares. Contudo, manchas de diferentes origens podem ser visualizadas nesse tipo de produ??o de imagem, tornando o monitoramento dif?cil. O sistema proposto neste trabalho, baseado em t?cnicas de processamento digital de imagens e redes neurais artificiais, tem o objetivo de identificar a mancha analisada e discernir entre ?leo e os demais fen?menos geradores de mancha. Testes nos blocos funcionais que comp?em o sistema proposto permitem a implementa??o de diferentes algoritmos, assim como sua an?lise detalhada e pontual. Os algoritmos que tratam do processamento digital de imagem (filtragem do ru?do speckle e gradiente), assim como o de classifica??o (Perceptron de M?ltiplas Camadas, rede de fun??o de Base Radial, M?quina de Vetor de Suporte e M?quina de comit?) s?o apresentados e comentados.O desempenho final do sistema, com diferentes tipos de classificadores, ? apresentado atrav?s da curva ROC. As taxas de acertos s?o consideradas condizentes com o que a literatura de detec??o de manchas de ?leo na superf?cie oce?nica atrav?s de imagens de SAR apresenta
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/15516 |
| Date | 24 July 2006 |
| Creators | Souza, Danilo Lima de |
| Contributors | CPF:10749896434, http://lattes.cnpq.br/1987295209521433, Brito J?nior, Agostinho de Medeiros, CPF:87830574472, http://lattes.cnpq.br/0958617290020120, Mata, Wilson da, D?ria Neto, Adri?o Duarte |
| Publisher | Universidade Federal do Rio Grande do Norte, Programa de P?s-Gradua??o em Engenharia El?trica, UFRN, BR, Automa??o e Sistemas; Engenharia de Computa??o; Telecomunica??es |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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