Information-theoretic assessment of sampled hyperspectral imagers

• Published in: IEEE transactions on geoscience and remote sensing 2001-07, Vol.39 (7), p.1447-1458
• Main Authors: Aiazzi, B., Alparone, L., Barducci, A., Baronti, S., Pippi, I.
• Format: Article
• Language: English
• Subjects: Assessments; Data compression; Digitization; Estimating; Gaussian noise; Hyperspectral imaging; Hyperspectral sensors; Image resolution; Image sensors; Loss measurement; Mathematical models; Noise; Noise measurement; Radiance; Signal to noise ratio; Spatial resolution; Spectral resolution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/36.934076

This work focuses on estimating the information conveyed to a user by hyperspectral image data. The goal is establishing the extent to which an increase in spectral resolution enhances the amount of usable information. Indeed, a tradeoff exists between spatial and spectral resolution due to physical constraints of multi-band sensors imaging with a prefixed SNR. After describing an original method developed for the automatic estimation of variance and correlation of the noise introduced by hyperspectral imagers, lossless interband data compression is exploited to measure the useful information content of hyperspectral 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 regarded as statistical uncertainty, but whose relevance to a user is null) and the intrinsic information of radiance sampled and digitized through an ideally noise-free process. An entropic model of the decorrelated image source is defined and, once the parameters of the noise, assumed to be Gaussian and stationary, have been measured, such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate. Results are reported and discussed on both simulated and AVIRIS data.