27 April 2009 Sensor-informed representation of hyperspectral images
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Abstract
Hyperspectral images are customarily stored and transferred as radiance values. Image analysis may benefit from additional sensor-related information such as signal-dependent noise levels. This paper discusses representations of hyperspectral image data in forms which are intermediate between raw data and radiance data. The intermediate-form data can be processed directly, or they can be readily converted into radiance values and estimates of signal-dependent noise. The metadata needed for this data transformation constitutes an informative first-order description of the sensor, as an added benefit for the data user. One of the proposed data formats has already been adopted in commercial hyperspectral sensors. The proposed representations can be stored in a more compact data format than radiance values without loss of information, under reasonable assumptions about the sensor properties. In particular, it will be shown that a square-root transformation of the data leads to a representation which approaches the information-theoretic lower limit for storing light samples. The use of noise estimates derived from sensor physics is likely to be useful in hyperspectral image processing and image compression.
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Torbjorn Skauli, Torbjorn Skauli, } "Sensor-informed representation of hyperspectral images", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 733418 (27 April 2009); doi: 10.1117/12.819491; https://doi.org/10.1117/12.819491
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