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Robust background representation is a key issue for detecting anomaly targets in hyperspectral imagery. Meanwhile, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection process. This paper for the first time aims to implement robust background representation, as well as to explore the intrinsic data structure of the hyperspectral imagery...
Localized training data typically utilized to develop a classifier may not be fully representative of class signatures over large areas but could potentially provide useful information which can be updated to reflect local conditions in other areas. An adaptive classification framework is proposed for this purpose, whereby a kernel machine is first trained with labeled data and then iteratively adapted...
A classifier derived from labeled samples acquired over an extended area may not perform well for a specific sub-region if the spectral signatures of classes vary across the image. However, characterizing the local effects are an ill-posed problem, particularly for hyperspectral data, since an adequate number of labeled samples is not typically available for every location. This problem is addressed...
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