title: Methods for Improving Image Quality and Reducing Data Load of NIR Hyperspectral Images.
abstract: Near Infrared Hyperspectral Imaging (NIRHSI) is an emerging technology platform that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Two important problems in NIRHSI are those of data load and unserviceable pixels in the NIR sensor. Hyperspectral imaging experiments generate large amounts of data (typically > 50 MB per image), which tend to overwhelm the memory capacity of conventional computer systems. This inhibits the utilisation of NIRHSI for routine online industrial application. In general, approximately 1% of pixels in NIR detectors are unserviceable or 'dead', containing no useful information. While this percentage of pixels is insignificant for single wavelength imaging, the problem is amplified in NIRHSI, where > 100 wavelength images are typically acquired. This paper describes an approach for reducing the data load of hyperspectral experiments by using sample-specific vector-to-scalar operators for real time feature extraction and a systematic procedure for compensating for 'dead' pixels in the NIR sensor. The feasibility of this approach was tested for prediction of moisture content in carrot tissue.
authors.affiliation: Corvinus University of Budapest, Faculty of Food Science, Department of Physics and Control, Somlóiút 14-16, H-1118 Budapest, Hungary. ferenc.firtha@uni-corvinus.hu.
authors.forename: Ferenc
authors.initials: F
authors.lastname: Firtha
authors.affiliation: Corvinus University of Budapest, Faculty of Food Science, Department of Physics and Control, Somlóiút 14-16, H-1118 Budapest, Hungary.
authors.forename: András
authors.initials: A
authors.lastname: Fekete
authors.affiliation: Corvinus University of Budapest, Faculty of Food Science, Department of Physics and Control, Somlóiút 14-16, H-1118 Budapest, Hungary.
authors.forename: Tímea
authors.initials: T
authors.lastname: Kaszab
authors.affiliation: Corvinus University of Budapest, Faculty of Food Science, Department of Physics and Control, Somlóiút 14-16, H-1118 Budapest, Hungary.
authors.forename: Bíborka
authors.initials: B
authors.lastname: Gillay
authors.affiliation: Corvinus University of Budapest, Faculty of Food Science, Department of Physics and Control, Somlóiút 14-16, H-1118 Budapest, Hungary.
authors.forename: Médea
authors.initials: M
authors.lastname: Nogula-Nagy
authors.affiliation: Corvinus University of Budapest, Faculty of Food Science, Department of Physics and Control, Somlóiút 14-16, H-1118 Budapest, Hungary.
authors.forename: Zoltán
authors.initials: Z
authors.lastname: Kovács
authors.affiliation: Corvinus University of Budapest, Faculty of Food Science, Department of Physics and Control, Somlóiút 14-16, H-1118 Budapest, Hungary.
authors.forename: David B
authors.initials: DB
authors.lastname: Kantor
keywords: Hyperspectral; carrot; data-extraction; moisture-content; noise