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Open AccessArticle

Methods for Improving Image Quality and Reducing Data Load of NIR Hyperspectral Images

Corvinus University of Budapest, Faculty of Food Science, Department of Physics and Control, Somlóiút 14-16, H-1118 Budapest, Hungary
Author to whom correspondence should be addressed.
Sensors 2008, 8(5), 3287-3298;
Received: 23 April 2008 / Accepted: 13 May 2008 / Published: 19 May 2008
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. View Full-Text
Keywords: Hyperspectral; noise; data-extraction; carrot; moisture-content Hyperspectral; noise; data-extraction; carrot; moisture-content
MDPI and ACS Style

Firtha, F.; Fekete, A.; Kaszab, T.; Gillay, B.; Nogula-Nagy, M.; Kovács, Z.; Kantor, D.B. Methods for Improving Image Quality and Reducing Data Load of NIR Hyperspectral Images. Sensors 2008, 8, 3287-3298.

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