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Proceedings Paper

Assessment of quality parameters for a new-generation hyperspectral imager
Author(s): Bruno Aiazzi; Luciano Alparone; Alessandro Barducci; Stefano Baronti; Donatella Guzzi; Paolo Marcoionni; Ivan Pippi; Massimo Selva
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Paper Abstract

This work focuses on an assessment of quality parameters characterizing a hyperspectral image collected by a new-generation high-resolution sensor named Hyper-SIMGA, which is a spectrometer operating in the push-broom configuration. By resorting to Shannon's information theory, the concept of quality is related to the information conveyed to a user by the hyperspectral data, which can be objectively defined from both the signal-to-noise ratio (SNR) and the mutual information between the unknown noise-free digitized signal and the corresponding noise-affected observed digital samples. The estimation of the mutual information has been exploited by resorting to a lossless data compression of the dataset. In fact, the bit-rate achieved by the reversible compression process is a suitable approximation of the decorrelated data entropy, which takes into account both the contribution of the "observation" noise, i.e. information regarded as statistical uncertainty, whose relevance is null to a user, and the intrinsic information of hypothetically noise-free samples. Noise estimation can be obtained once a suitable parametric model of the noise, assumed to be possibly non-Gaussian, has been preliminarily determined. Noise amplitude has been assessed by means of two independent estimators relying on two automatic procedures based on a scatterplot method and a bit-plane algorithm. Noise autocorrelation has been taken into account on the three allowed directions of the available data-volume. Results are reported and discussed employing a hyperspectral image (768 spectral bands) recorded by the new Hyper-SIMGA imaging spectrometer.

Paper Details

Date Published: 30 October 2007
PDF: 12 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480Q (30 October 2007); doi: 10.1117/12.739486
Show Author Affiliations
Bruno Aiazzi, Istituto di Fisica Applicata Nello Carrara (Italy)
Luciano Alparone, Univ. degli Studi di Firenze (Italy)
Alessandro Barducci, Istituto di Fisica Applicata Nello Carrara (Italy)
Stefano Baronti, Istituto di Fisica Applicata Nello Carrara (Italy)
Donatella Guzzi, Istituto di Fisica Applicata Nello Carrara (Italy)
Paolo Marcoionni, Istituto di Fisica Applicata Nello Carrara (Italy)
Ivan Pippi, Istituto di Fisica Applicata Nello Carrara (Italy)
Massimo Selva, Istituto di Fisica Applicata Nello Carrara (Italy)

Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)

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