Análise comparativa de Wavelets para detecção de fenômenos oceanográficos relacionados à temperatura de superfície do mar em imagens adquiridas por satélite

• Ano de defesa: 2006
• Autor(a) principal: Souza, Paulo Roberto Nunes de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Informática; Centro Tecnológico; UFES; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Oceanography; Remote sensing; Image processing; Ondaletas; Oceanografia; Sensoriamento remoto; Wavelets (Matemática); Processamento de imagens; Ciência da Computação; 004
• Link de acesso: http://repositorio.ufes.br/handle/10/6370

The Remote Sensing arouses interest from general people, productive sector and research. Several fields can benefit of analytic and virtually omnipresent view of the satellites. When applied to oceanography, the advantages of remote sensing over traditional methods are as big as the oceans. With the possibility to acquire images of virtually anywhere over the planet, the challenge becomes to analyze these data and extract important information. The explored method in this work was to extract information using signal spectral analysis techniques, especially Wavelet Transform. The defined objectives were: to evaluate the potential of Wavelet Analysis to identify oceanographic phenomena related to the Sea Surface Temperature on digital images acquired from satellites, to define a methodology to choose Wavelet functions to be used and to compare the results of the chosen Wavelets. As digital images from satellites were discrete data, it was used a methodology based on the Discrete Wavelet Analyses. The Discrete Wavelet Analyses uses the filter banks theory, where the original data is processed by a set of filters to result in a Wavelet processed data. The chosen Wavelet Function defines the set of filters to be used. At the used methodology the filter bank was applied to blocks of the original images, generating results representing the wavelet application to each block. The processed data was then submitted to a classification function. This function grouped the blocks based at the wavelet result and a previously chosen threshold. At the end of the process there were an image proportionally segmented to the response of the Wavelet applied to each block of the original image. Using this methodology it was possible to segment digital images of the Sea Surface Temperature acquired by satellites, providing a way to identify several thermal phenomena on the ocean surface a classify the used Wavelets according to the results quality of them.