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dc.contributor.authorPhillips, Rhonda D.en
dc.contributor.authorBlinn, Christine E.en
dc.contributor.authorWatson, Layne T.en
dc.contributor.authorWynne, Randolph H.en
dc.date.accessioned2013-06-19T14:37:06Zen
dc.date.available2013-06-19T14:37:06Zen
dc.date.issued2008en
dc.identifierhttp://eprints.cs.vt.edu/archive/00001042/en
dc.identifier.urihttp://hdl.handle.net/10919/19373en
dc.description.abstractThis paper describes a new algorithm used to adaptively filter a remote sensing dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) has been applied. This algorithm uses Hermite splines to calculate the approximate area underneath the SNR curve as a function of band number, and that area is used to place bands into “bins” with other bands having similar SNRs. A median filter with a variable sized kernel is then applied to each band, with the same size kernel used for each band in a particular bin. The proposed adaptive filters are applied to a hyperspectral image generated by the AVIRIS sensor, and results are given for the identification of three different pine species located within the study area. The adaptive filtering scheme improves image quality as shown by estimated SNRs, and classification accuracies improved by more than 10% on the sample study area, indicating that the proposed methods improve the image quality, thereby aiding in species discrimination.en
dc.format.mimetypeapplication/pdfen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAlgorithmsen
dc.subjectData structuresen
dc.titleAn Adaptive Noise Filtering Algorithm for AVIRIS Data with Implications for Classification Accuracyen
dc.typeTechnical reporten
dc.contributor.departmentCenter for Environmental Applications of Remote Sensing (CEARS)en
dc.contributor.departmentComputer Scienceen
dc.identifier.trnumberTR-08-15en
dc.type.dcmitypeTexten
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001042/01/mnf08.pdfen


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