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We propose a low false alarm methodology to determine anomalies in hyperspectral data. The method is based on the assumptions that the linear mixing model is valid and that, due to the resolution of the image, most pixels are mixtures of common substances, of which pure pixels (not mixtures) are rare. In the first stage of the algorithm, the classes associated with the background, which are the dominant...
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