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27 April 2009 Hyperspectral texture synthesis by multiresolution pyramid decomposition
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Hyperspectral images have textured regions and in many cases there are not sufficient samples to train classifiers. By simulating more samples that are self-similar to the original texture efficient classifiers can be trained and used for classification. The first step toward this goal is to develop a hyperspectral texture synthesis algorithm that efficiently combines both the spectral information and the spatial variability in the original image. We propose a method for texture region synthesis using neighboring pixel information, as well as interband correlation. The synthesis is done by a neighborhood search of a multiresolution pyramid constructed from the original sample that encodes the spectrum and spatial intensity gradients. Gaussian decomposition is used for the multiresolution decomposition. The pyramid is a representation of the hyperspectral image as a compact code. Starting from a seed image, the neighborhood is synthesized by sampling of the pyramid for the nearest neighbors. Results of the synthesis are presented using synthetic and remote sensing hyperspectral images. Discriminant analysis of texture properties of the synthesized texture are compared with the original texture and classification results are presented with different hyperspectral scenarios.
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Nestor J. Diaz and Vidya Manian "Hyperspectral texture synthesis by multiresolution pyramid decomposition", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340Q (27 April 2009);

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