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Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison
Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly.
Study of the gOMP Algorithm for Recovery of Compressed Sensed Hyperspectral Images
It is concluded that the gOMP algorithm reconstructs the hyperspectral images with higher accuracy as well as faster convergence when the pixels are highly sparsified and hence at the expense of reducing the quality of the recovered images with respect to the original images.
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction
Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission.
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