http://www.cnr.it/ontology/cnr/individuo/prodotto/ID22504

Estimating Noise and Information of Multispectral Imagery (Articolo in rivista)

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Estimating Noise and Information of Multispectral Imagery (Articolo in rivista) (literal)

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Alternative label

Bruno Aiazzi; Luciano Alparone; Alessandro Barducci; Stefano Baronti; Ivan Pippi (2002)
Estimating Noise and Information of Multispectral Imagery
in Optical engineering (Bellingham, Print); SPIE-Society of Photo-optical Instrumentation Engineers, Bellingham (Stati Uniti d'America)
(literal)

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Bruno Aiazzi; Luciano Alparone; Alessandro Barducci; Stefano Baronti; Ivan Pippi (literal)

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National Research Council (CNR), \"Nello Carrara\" Research Institute on Electromagnetic Waves (IROE), I-50127 Florence, Italy University of Florence, Department of Electronics and Communications, I-50139 Florence, Italy National Research Council (CNR), \"Nello Carrara\" Research Institute on Electromagnetic Waves (IROE), I-50127 Florence, Italy National Research Council (CNR), \"Nello Carrara\" Research Institute on Electromagnetic Waves (IROE), I-50127 Florence, Italy National Research Council (CNR), \"Nello Carrara\" Research Institute on Electromagnetic Waves (IROE), I-50127 Florence, Italy (literal)

Titolo

Abstract

This work focuses on reliably estimating the information conveyed to a user by multispectral image data. The goal is establishing the extent to which an increase in spectral resolution can increase the amount of usable information. As a matter of fact, a tradeoff exists between spatial and spectral resolution, due to physical constraints of sensors imaging with a prefixed SNR. After describing some methods developed for automatically estimating the variance of the noise introduced by multispectral imagers, lossless data compression is exploited to measure the useful information content of the multispectral data. In fact, the bit rate achieved by the reversible compression process takes into account both the contribution of the \"observation\" noise, i.e., information egarded as statistical incertainty, whose relevance is null to a user, and the intrinsic information of hypothetically noise-free multispectral data. An entropic model of the image source is defined and, once the standard deviation of the noise, assumed to be white and Gaussian, has been preliminarily estimated, such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate. Results of both noise and information assessment are reported and discussed on synthetic noisy images and on Landsat TM data. (literal)

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