Recent progress on radiation detection from near-infrared to mid-infrared based on frequency upconversion

2010 ◽  
Author(s):  
Yi Jiang ◽  
Yujie J. Ding ◽  
Ioulia B. Zotova ◽  
Narasimha S. Prasad
Keyword(s):  
Recent Progress ◽  
Near Infrared ◽  
Radiation Detection ◽  
Mid Infrared ◽  
Frequency Upconversion

Monolithically integrated near-infrared and mid-infrared detector array for spectral imaging

2007 ◽  
Vol 50 (2-3) ◽  
pp. 211-216 ◽  
Author(s):  
S.V. Bandara ◽  
S.D. Gunapala ◽  
D.Z. Ting ◽  
J.K. Liu ◽  
C.J. Hill ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Detector ◽  
Spectral Imaging ◽  
Detector Array ◽  
Mid Infrared ◽  
Monolithically Integrated ◽  
Infrared Detector Array

Mid and near Infrared Study of Carbohydrates by Canonical Correlation Analysis

1993 ◽  
Vol 1 (2) ◽  
pp. 99-108 ◽  
Author(s):  
P. Robert ◽  
M.F. Devaux ◽  
A. Qannari ◽  
M. Safar
Keyword(s):  
Correlation Analysis ◽  
Infrared Spectra ◽  
Canonical Correlation ◽  
Near Infrared ◽  
Sugar Content ◽  
Infrared Region ◽  
Total Sugar ◽  
Mid Infrared ◽  
Total Sugar Content ◽  
Near Infrared Spectra

Multivariate data treatments were applied to mid and near infrared spectra of glucose, fructose and sucrose solutions in order to specify near infrared frequencies that characterise each carbohydrate. As a first step, the mid and near infrared regions were separately studied by performing Principal Component Analyses. While glucose, fructose and sucrose could be clearly identified on the similarity maps derived from the mid infrared spectra, only the total sugar content of the solutions was observed when using the near infrared region. Characteristic wavelengths of the total sugar content were found at 2118, 2270 and 2324 nm. In a second step, the mid and near infrared regions were jointly studied by a Canonical Correlation Analysis. As the assignments of frequencies are generally well known in the mid infrared region, it should be useful to study the relationships between the two infrared regions. Thus, the canonical patterns obtained from the near infrared spectra revealed wavelengths that characterised each carbohydrate. The OH and CH combination bands were observed at: 2088 and 2332 nm for glucose, 2134 and 2252 nm for fructose, 2058 and 2278 nm for sucrose. Although a precise assignment of the near infrared bands to chemical groups within the molecules was not possible, the present work showed that near infrared spectra of carbohydrates presented specific features.

scholarly journals Mapping Sub-Metre 3D Land-Sea Coral Reefscapes Using Superspectral WorldView-3 Satellite Stereoimagery

Oceans ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 315-329
Author(s):  
Antoine Collin ◽  
Mark Andel ◽  
David Lecchini ◽  
Joachim Claudet
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Sustainable Management ◽  
Near Infrared ◽  
Value Added ◽  
Land Use Land Cover ◽  
Fine Scale ◽  
Mid Infrared ◽  
Stereo Imagery ◽  
Sea Use

Shallow coral reefs ensure a wide portfolio of ecosystem services, from fish provisioning to tourism, that support more than 500 million people worldwide. The protection and sustainable management of these pivotal ecosystems require fine-scale but large-extent mapping of their 3D composition. The sub-metre spaceborne imagery can neatly produce such an expected product using multispectral stereo-imagery. We built the first 3D land-sea coral reefscape mapping using the 0.3 m superspectral WorldView-3 stereo-imagery. An array of 13 land use/land cover and sea use/sea cover habitats were classified using sea-, ground- and air-truth data. The satellite-derived topography and bathymetry reached vertical accuracies of 1.11 and 0.89 m, respectively. The value added of the eight mid-infrared (MIR) channels specific to the WorldView-3 was quantified using the classification overall accuracy (OA). With no topobathymetry, the best combination included the eight-band optical (visible + near-infrared) and the MIR8, which boosted the basic blue-green-red OA by 9.58%. The classes that most benefited from this MIR information were the land use “roof” and land cover “soil” classes. The addition of the satellite-derived topobathymetry to the optical+MIR1 produced the best full combination, increasing the basic OA by 9.73%, and reinforcing the “roof” and “soil” distinction.

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