Modeling and estimation of signal-dependent noise in hyperspectral imagery

The majority of hyperspectral data exploitation algorithms are developed using statistical models for the data that include sensor noise. Hyperspectral data collected using charge-coupled devices or other photon detectors have sensor noise that is directly dependent on the amplitude of the signal co...

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Published inApplied optics. Optical technology and biomedical optics Vol. 50; no. 21; p. 3829
Main Authors Meola, Joseph, Eismann, Michael T, Moses, Randolph L, Ash, Joshua N
Format Journal Article
LanguageEnglish
Published United States 20.07.2011
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Abstract The majority of hyperspectral data exploitation algorithms are developed using statistical models for the data that include sensor noise. Hyperspectral data collected using charge-coupled devices or other photon detectors have sensor noise that is directly dependent on the amplitude of the signal collected. However, this signal dependence is often ignored. Additionally, the statistics of the noise can vary spatially and spectrally as a result of camera characteristics and the calibration process applied to the data. Here, we examine the expected noise characteristics of both raw and calibrated visible/near-infrared hyperspectral data and provide a method for estimating the noise statistics using calibration data or directly from the imagery if calibration data is unavailable.
AbstractList The majority of hyperspectral data exploitation algorithms are developed using statistical models for the data that include sensor noise. Hyperspectral data collected using charge-coupled devices or other photon detectors have sensor noise that is directly dependent on the amplitude of the signal collected. However, this signal dependence is often ignored. Additionally, the statistics of the noise can vary spatially and spectrally as a result of camera characteristics and the calibration process applied to the data. Here, we examine the expected noise characteristics of both raw and calibrated visible/near-infrared hyperspectral data and provide a method for estimating the noise statistics using calibration data or directly from the imagery if calibration data is unavailable.
Author Meola, Joseph
Eismann, Michael T
Moses, Randolph L
Ash, Joshua N
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/21772364$$D View this record in MEDLINE/PubMed
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