Abstract
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.
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