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.