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Sparse representation of signals has been recently applied for hyperspectral imagery classification. It relies on the assumption that a test pixel can be linearly and sparsely represented as a combination of all training samples. Although recent work reported in the literature has exploited the sparsity of hyperspectral images, its use in effective multiscale representation of hyperspectral imagery...
Hyperspectral imagery (HSI) consists of hundred or thousand of spectral bands and provides a wealth of spectral information. Although several parametric and non-parametric classifiers have been built with the purpose of efficiently and fully utilizing the spectral information in HSI, few of them are designed to exploit the underlying sparsity in HSI. In recent work, a joint sparsity model (JSM) based...
<?Pub Dtl?>Recent developments in remote sensing technologies have made hyperspectral imagery (HSI) readily available to detect and classify objects on the earth using pattern recognition techniques. Hyperspectral signatures are composed of densely sampled reflectance values over a wide range of the spectrum. Although most of the traditional approaches for HSI analysis entail per-pixel spectral...
Hyperspectral imaging (HSI) techniques have been widely used for a variety of applications pertaining to vegetation species identification. With its rich spectral information, HSI is a powerful tool to detect and characterize vegetation species and their health. However, due to the high dimensionality of HSI, a the number of training samples required to estimate the parameters of the automated target...
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