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TitleImpact of spectral curvature on at-surface reflectance accuracy and information extraction techniques
AuthorWhite, H PORCID logo
SourceProceedings of the Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2011; 2011 p. 1-5,
Alt SeriesEarth Sciences Sector, Contribution Series 20110045
Meeting5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2011; Lisbon; PT; June 6-9, 2011
Mediaon-line; digital; paper
File formatpdf
Subjectsgeophysics; remote sensing; modelling
Illustrationsgraphs; plots; tables
ProgramRemote Sensing Science
AbstractIn Canada's arctic, the landscape is a complex mixture of exposed rock with varying levels of lichen and tundra vegetation, illuminated by a low elevation sun. Imaging spectrometry (hyperspectral remote sensing) has been shown useful for mapping northern environments, with applications in geological exploration and habitat mapping. Use of hyperspectral imagery aims to exploit spectral characteristics unique to surface cover classes. This assumes pixels provide a linear combination of spectral reflectance from existing sub-pixel components, referred to as endmembers. The level of success can be directly impacted by data quality. Insuring the highest quality data is being used requires a robust pre-processing system which evaluates known artefacts, such as spectral curvature (spectral smile),
noise, and spectral gain error. In many systems, spectral smile is often the most overlooked source of uncertainty. To evaluate one aspect of hyperspectral imagery for northern environments, the impact of spectral smile is explored with relation to spectral indices which are used to discriminate between surface material types.

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