Abstract
A formulation of hyperspectral images as function-valued mappings is introduced, along with a set of simple models of affine self-similarity for digital hyperspectral images. As in the case of greyscale images, these models examine how well vector-valued image subblocks are approximated by other subblocks, as measured by the distribution of approximation errors. This set of models includes both same-scale and cross-scale modes of approximation, the latter of which provides the basis of a method of fractal transforms over hyperspectral images.
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Vrscay, E.R., Otero, D., La Torre, D. (2013). Hyperspectral Images as Function-Valued Mappings, Their Self-similarity and a Class of Fractal Transforms. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_26
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DOI: https://doi.org/10.1007/978-3-642-39094-4_26
Publisher Name: Springer, Berlin, Heidelberg
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