Diaz-Gonzalez, Nestor J.

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    Hyperspectral texture synthesis algorithms
    (2010) Diaz-Gonzalez, Nestor J.; Manian, Vidya; College of Engineering; Vélez-Reyes, Miguel; Rodríguez, Domingo; Department of Electrical and Computer Engineering; Castillo, Paul
    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. This kind of tools provides a means to get synthetic data without the expense of data collection. 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. Three algorithms are implemented to fulfill that purpose. To do the synthesis, the first algorithm uses neighboring pixel information, and this 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 second approach is as simple as putting together blocks with a certain overlap, so that each new block is chosen so that it “agrees” with its neighbors in the region of overlap. A third implementation uses 3D wavelet transform of the original sample. This encodes the spectrum and spatial intensity gradients. The wavelet coefficients are a representation of the hyperspectral image as a compact code. These coefficients are then synthesized by an image quilting algorithm. Results of the synthesis are presented using hyperspectral images from three different remote sensing scenarios. Discriminant analysis and support vector classifier are used to classify the synthesized textures. It was verified that there is a substantial agreement between the input textures and the synthetic ones.