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dc.contributor.authorSkauli, Torbjørn
dc.date.accessioned2017-10-30T13:19:56Z
dc.date.accessioned2017-10-31T09:27:31Z
dc.date.available2017-10-30T13:19:56Z
dc.date.available2017-10-31T09:27:31Z
dc.date.issued2011
dc.identifier.citationSkauli T. Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing. Optics Express. 2011;19(14):13031-13046en_GB
dc.identifier.urihttp://hdl.handle.net/20.500.12242/759
dc.identifier.urihttps://ffi-publikasjoner.archive.knowledgearc.net/handle/20.500.12242/759
dc.descriptionSkauli, Torbjørn. Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing. Optics Express 2011 ;Volum 19.(14) s. 13031-13046en_GB
dc.description.abstractMany types of hyperspectral image processing can benefit from knowledge of noise levels in the data, which can be derived from sensor physics. Surprisingly, such information is rarely provided or exploited. Usually, the image data are represented as radiance values, but this representation can lead to suboptimal results, for example in spectral difference metrics. Also, radiance data do not provide an appropriate baseline for calculation of image compression ratios. This paper defines two alternative representations of hyperspectral image data, aiming to make sensor noise accessible to image processing. A "corrected raw data" representation is proportional to the photoelectron count and can be processed like radiance data, while also offering simpler estimation of noise and somewhat more compact storage. A variance-stabilized representation is obtained by square-root transformation of the photodetector signal to make the noise signal-independent and constant across all bands while also reducing data volume by almost a factor 2. Then the data size is comparable to the fundamental information capacity of the sensor, giving a more appropriate measure of uncompressed data size. It is noted that the variance-stabilized representation has parallels in other fields of imaging. The alternative data representations provide an opportunity to reformulate hyperspectral processing algorithms to take actual sensor noise into account. (C) 2011 Optical Society of Americaen_GB
dc.language.isoenen_GB
dc.subjectHyperspektral avbildning
dc.subjectStøy
dc.titleSensor noise informed representation of hyperspectral data, with benefits for image storage and processingen_GB
dc.typeArticleen_GB
dc.date.updated2017-10-30T13:19:56Z
dc.identifier.cristinID844894
dc.identifier.cristinID844894
dc.identifier.doi10.1364/OE.19.013031
dc.source.issn1094-4087
dc.type.documentJournal article
dc.relation.journalOptics Express


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