Keyword search (4,163 papers available)

"Thörnberg B" Authored Publications:

Title Authors PubMed ID
1 Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging Shaikh MS; Jaferzadeh K; Thörnberg B; 35270968
ENCS
2 Calibration of a Hyper-Spectral Imaging System Using a Low-Cost Reference Shaikh MS; Jaferzadeh K; Thörnberg B; Casselgren J; 34072156
ENCS

 

Title:Calibration of a Hyper-Spectral Imaging System Using a Low-Cost Reference
Authors:Shaikh MSJaferzadeh KThörnberg BCasselgren J
Link:https://pubmed.ncbi.nlm.nih.gov/34072156/
DOI:10.3390/s21113738
Publication:Sensors (Basel, Switzerland)
Keywords:InGaAsPTFEcalibrationdark currenthyperspectral imagingpush-broom cameraspectralonteflonwinter road conditions
PMID:34072156 Category: Date Added:2021-06-02
Dept Affiliation: ENCS
1 Department of Electronics Design, Mid Sweden University, 851 70 Sundsvall, Sweden.
2 Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
3 Department of Engineering Sciences and Mathematics, Luleå University of Technology, 971 87 Luleå, Sweden.

Description:

In this paper, we present a hyper-spectral imaging system and practical calibration procedure using a low-cost calibration reference made of polytetrafluoroethylene. The imaging system includes a hyperspectral camera and an active source of illumination with a variable spectral distribution of intensity. The calibration reference is used to measure the relative reflectance of any material surface independent of the spectral distribution of light and camera sensitivity. Winter road conditions are taken as a test application, and several spectral images of snow, icy asphalt, dry asphalt, and wet asphalt were made at different exposure times using different illumination spectra. Graphs showing measured relative reflectance for different road conditions support the conclusion that measurements are independent of illumination. Principal component analysis of the acquired spectral data for road conditions shows well separated data clusters, demonstrating the system's suitability for material classification.





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