Presentation + Paper
18 September 2018 MSI vs. HSI in cultural heritage imaging
Tyler R. Peery, David Messinger
Author Affiliations +
Abstract
The goal of this research was to compare the noise between the current MSI (multispectral imaging) R-CHIVE (Rochester Cultural Heritage Imaging, Visualization, and Education) system with various HSI (hyperspectral imaging) systems. Because most archived images are visually analyzed by humanities scholars, human vision factors are considered as limiting factors in both spatial resolution and minimum detectable noise levels for possible use as system design requirements. A major part of the image modeling analysis focused on the spatial/spectral compromise inherent in HSI systems and the impact of typical broadband sources' inefficiency in the visible spectrum. The peak LED spectral irradiance was found to be 180× greater than the broadband peak irradiance, and more than 2000× that of the visible region. This resulted in an increased scan time from 8 minutes to 2 hours if spatial resolution is required to be maintained at current levels for similar bands. Instituting an acceptable loss ratio from the very high resolution MSI system resulted in peak SNR values being maintained in as short as 5 seconds disregarding readout time. Signal to noise ratio (SNR) decreased from 106 in the MSI system to a peak of 57 in the modeled HSI system, following the well depth, or analog dynamic range of the sensor, as expected in a photon-limited scene.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tyler R. Peery and David Messinger "MSI vs. HSI in cultural heritage imaging", Proc. SPIE 10768, Imaging Spectrometry XXII: Applications, Sensors, and Processing, 107680G (18 September 2018); https://doi.org/10.1117/12.2320671
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Signal to noise ratio

Sensors

Imaging systems

Multispectral imaging

Light emitting diodes

Systems modeling

Signal detection

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