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Advanced Spectral Classifiers for Hyperspectral Images: A review

Pedram Ghamisi, Javier Plaza, Yushi Chen, Jun Li, Antonio Plaza

📄 Abstract

Hyperspectral image classification has been a vibrant area of research in recent years. Given a set of observations, i.e., pixel vectors in a hyperspectral image, classification approaches try to allocate a unique label to each pixel vector. However, the classification of hyperspectral images is a challenging task for a number of reasons, such as the presence of redundant features, the imbalance among the limited number of available training samples, and the high dimensionality of the data.

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