There has been an exponential increase in satellite image data availability. Image data are now collected with different spatial, spectral, and temporal resolutions. Image fusion techniques are used extensively to combine different images having complementary information into one single composite. The fused image has rich information that will improve the performance of image analysis algorithms. Pansharpening is a pixel level fusion technique used to increase the spatial resolution of the multispectral image using spatial information from the high resolution panchromatic image while preserving the spectral information in the multispectral image. Resolution merge, image integration, and multisensor data fusion are some of the equivalent terms used for pansharpening. Pansharpening techniques are applied for enhancing certain features not visible in either of the single data alone, change detection using temporal data sets, improving geometric correction, and enhancing classification. Various pansharpening algorithms are available in the literature, and some have been incorporated in commercial remote sensing software packages such as ERDAS Imagine® and ENVI®. The performance of these algorithms varies both spectrally and spatially. Hence evaluation of the spectral and spatial quality of the pansharpened images using objective quality metrics is necessary. In this thesis, quantitative metrics for evaluating the quality of pansharpened images have been developed. For this study, the Intensity-Hue-Saturation (IHS) based sharpening, Brovey sharpening, Principal Component Analysis (PCA) based sharpening and a Wavelet-based sharpening method is used.

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Made available in DSpace on 2016-06-02T19:03:57Z (GMT). No. of bitstreams: 1 5415.pdf: 8638421 bytes, checksum: 0e5c5abf95c786434202fdae3e69dc1e (MD5) Previous issue date: 2013-06-24 / Financiadora de Estudos e Projetos / The resolution enhancement of an image is always desirable, independently of its objective, but mainly if the image has the purpose of visual analysis. The hardware development for increasing the image resolution still has a higher cost than the algorithmic solutions for super-resolution. Like image restoration, super-resolution is also an ill-conditioned inverse problem, and has an infinite number of solutions. This work analyzes the iterative restoration methods (Van Cittert, Tikhonov-Miller and Conjugate Gradiente) which propose solutions for the ill-conditioning problem and compares them with the IBP method (Iterative Back Projection). The analysis of the found similarities is the basis of a generalization, such that other iterative restoration methods can have their properties adapted, as regularization of the ill-conditioning, noise reduction and other degradations and the increase of the convergence rate can be incorporated to the techniques of super-resolution. Two new methods were created as case studies of the proposed generalization: the first one is a super-resolution method for dynamic magnetic resonance imaging (MRI) of the swallowing process, that uses an adaptiveWiener filtering as regularization and a non-rigid registration; and the second one is a pan sharpening method of SPOT satellite bands, that uses sampling based on sensor s characteristics and non-adaptive Wiener filtering. / A melhora da resolução de uma imagem é sempre desejada, independentemente de seu objetivo, mas principalmente se destinada a análise visual. O desenvolvimento de hardware para o aumento de resolução de uma imagem em sua captura ainda possui o custo mais elevado do que as soluções algorítmicas de super resolução (SR). Assim como a restauração de imagens, a super resolução também é um problema inverso mal-condicionado e possui infinitas soluções. Este trabalho analisa métodos de restauração iterativos (Van Cittert, Tikhonov-Miller e Gradiente Conjugado) que proponham soluções para o problema do malcondicionamento e os compara com o método IBP (Iterative Back-Projection). A análise das semelhanças encontradas é base para uma generalização de modo que outros métodos iterativos de restauração possam ter suas propriedades adaptadas, tais como regularização do mal-condicionamento, redução do ruído e outras degradações e aumento na taxa de convergência, para que possam ser incorporadas à técnicas de super resolução. Dois novos métodos foram criados como estudo de caso da generalização proposta: o primeiro é um método de super-resolução para imageamento por ressonância magnética (MRI) dinâmico do processo de deglutição, que utiliza uma filtragem de Wiener adaptativa como regularização e registro não-rígido; o segundo é um método de pansharpening das bandas do satélite SPOT, que utiliza amostragem baseada nas características do sensor e filtragem de Wiener não-adaptativa.