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Acquired images in hyperspectral imagery are disturbed by additive noise which is usually assumes as zero-mean white one. In fact, there is still non-white noise in hyperspectral images (HSIs). The 2-dimensional filtering methods and multidimensional tensor decomposition algorithms cannot be used to remove non-white noise from HSIs directly. Therefore, a prewhitening denoising solution based on tensor...
We examine the properties and performance of kernelized anomaly detectors, with an emphasis on the Mahalanobis-distance-based kernel RX (KRX) algorithm. Although the detector generally performs well for high-bandwidth Gaussian kernels, it exhibits problematic (in some cases, catastrophic) performance for distances that are large compared to the bandwidth. By comparing KRX to two other anomaly detectors,...
Hyperspectral (HS) imaging is a complex way of taking the image of the scenery, where the rich spectral information is collected for any pixel of HS image. The rich spectral information can consequently be used for finding objects or identifying specific material in military utilization of HS imaging. Finding the objects as a spectral anomaly is one of specific tasks of HS image processing.
When performing blind unmixing of hyperspectral images, the mixing model properties determine which unmixing methods are adequate and how their parameters should be set. One should therefore derive these properties before unmixing, directly from the observed mixed data. We show how to deduce such properties from the eigenvalues of the correlation and covariance matrices of observed hyperspectral images,...
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