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TitlePreprocessing of EO-1 Hyperion data
 
AuthorKhurshid, K S; Staenz, K; Sun, L; Neville, R; White, H PORCID logo; Bannari, A; Champagne, C M; Hitchcock, R
Source26th Canadian Symposium on Remote Sensing: managing resources and monitoring the environment; by Hopkinson, C (ed.); White, H PORCID logo (ed.); Canadian Journal of Remote Sensing vol. 32, no. 2, 2006 p. 84-97, https://doi.org/10.5589/m06-014
Year2006
Alt SeriesEarth Sciences Sector, Contribution Series 20060108
PublisherInforma UK Limited
Meeting26th Canadian Symposium on Remote Sensing: managing resources and monitoring the environment; CA
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf (Adobe Acrobat Reader)
Subjectsmiscellaneous; remote sensing; spectral analyses; satellites; satellite imagery; spectrometric analyses; infrared surveys; infrared spectral analyses; software; computer graphics; computer mapping; computer applications; data collections
Illustrationsschematic diagrams; formulae; aerial photographs; satellite imagery; satellite images; graphs; tables
ProgramGeomatics for Sustainable Development of Natural Resources
ProgramSustainable Development Through Knowledge Integration
Released2014 06 02
AbstractA procedure for processing hyperspectral data acquired with Hyperion has been developed with an aim to correct for sensor artifacts and atmospheric and geometric effects. Advances in preprocessing of hyperspectral remote sensing data have enabled more accurate atmospheric correction and have led to the development of new information extraction techniques in the areas of agriculture, forestry, geosciences, and environmental monitoring. These processing and analysis tools have been incorporated into Imaging Spectrometer Data Analysis Systems (ISDAS), a software package developed at the Canada Centre for Remote Sensing (CCRS). The procedure, as applied for Hyperion data, begins with geometric corrections to the short-wave infrared (SWIR) component to register the SWIR and visible near-infrared (VNIR) data spatially. This is followed by the removal of stripes and pixel (column) dropouts and noise reduction, using recently developed automated software tools. The data cube is subsequently analyzed using keystone and spectral smile detection software to characterize these distortions. Included in the smile detection procedure is an optional gain and offset correction technique. The radiance data are converted to reflectance using a MODTRAN-based atmospheric correction procedure. Only at this point are the data corrected for smile effects. Any artifacts still remaining after these corrections are removed by post-processing.
GEOSCAN ID222382

 
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