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The problem of assessing the noise amplitude affecting remotely sensed hyperspectral images and the corresponding signal-to-noise ratio is discussed. An original algorithm for noise estimation, which performs the analysis of image bit-planes in order to assess their randomness, is described. Differently from more traditional signal-to-noise estimators, which need a homogeneous area in the concerned image to isolate noise contributions, this estimator is almost insensitive to scene texture, a circumstance that allows the developed method to carefully assess the noise amplitude of nearly any observed targets. The developed algorithm has been compared with the well-known noise estimator scatterplot method, for which a novel implementation based on the Hough transform is presented. Hyperspectral and multispectral data cubes collected by the following aerospace imagers, MIVIS, VIRS-200, and MOMS-2P on PRIRODA, have been utilized for investigating the performance of the two considered estimators. Outcomes from processing synthetic and natural images are presented and discussed along this paper. [ABSTRACT FROM AUTHOR]

The problem of temperature and spectral emissivity assessment from hyperspectral remotely sensed data is discussed with reference to monitoring of active fires and hot targets. A new algorithm, called similar pixel addition, was developed, which allows us to retrieve the temperature of burning areas by employing spectral data collected at thermal infrared (TIR) wavelengths. The new algorithm resolves the uncertainty connected with temperature-emissivity separation assuming a slow spatial variation of emissivity, hence reducing the number of unknowns involved in the inversion of a couple of similar pixels at once. Performance of this procedure is thoroughly discussed and compared with results from two other algorithms operating in the TIR and shortwave infrared spectral ranges. This paper shows results obtained applying the new algorithm to hyperspectral images gathered by the Multispectral Infrared and Visible Imaging Spectrometer in Northern Italy (Alps) over a natural fire that broke out in July 1999. This paper is completed with a theoretical discussion of the involved topics. [ABSTRACT FROM AUTHOR]