Open Access
| Issue |
J. Eur. Opt. Society-Rapid Publ.
Volume 19, Number 2, 2023
EOSAM 2022
|
|
|---|---|---|
| Article Number | 34 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/jeos/2023027 | |
| Published online | 19 June 2023 | |
- Khan M.J., Khan H.S., Yousaf A., Khurshid K., Abbas A. (2018) Modern trends in hyperspectral image analysis: A review, IEEE Access 6, 14118. [CrossRef] [Google Scholar]
- Hilton F., Armante R., August T., Barnet C., Bouchard A., Camy-Peyret C., Capelle V., Clarisse L., Clerbaux C., Coheur P.F., Collard A. (2012) Hyperspectral earth observation from IASI: Five years of accomplishments, Bull. Am. Meteorol. Soc. 93, 347. [CrossRef] [Google Scholar]
- Carrasco O., Gomez R.B., Chainani A., Roper W.E. (2003) Hyperspectral imaging applied to medical diagnoses and food safety, in: Faust N.L., Roper W.E. (eds), Geo-spatial and temporal image and data exploitation III, 21, 24 April, 2003, Orlando, FL, USA. SPIE. [Google Scholar]
- Calin M.A., Parasca S.V., Savastru D., Manea D. (2013) Hyperspectral imaging in the medical field: Present and future, Appl. Spectrosc. Rev. 49, 435. [Google Scholar]
- Picon A., Ghita O., Iriondo P.M., Bereciartua A., Whelan P.F. (2010) Automation of waste recycling using hyperspectral image analysis, in: 2010 IEEE 15th Conference on Emerging Technologies & Factory Automation (ETFA 2010), 13–16 September 2010, Bilbao, Spain. IEEE. [Google Scholar]
- Feng Y.Z., Sun D.W. (2012) Application of hyperspectral imaging in food safety inspection and control: A review, Crit. Rev. Food Sci. Nutr. 52, 1039. [CrossRef] [Google Scholar]
- Behmann J., Acebron K., Emin D., Bennertz S., Matsubara S., Thomas S., Bohnenkamp D., Kuska M., Jussila J., Salo H., Mahlein A.K. (2018) Specim IQ: Evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection, Sensors 18, 441. [NASA ADS] [CrossRef] [Google Scholar]
- Levin P., Ashkenazy E., Raz A., Hershcovitz M., Bouwstra S., Mendlovic D., Krylov S. (2019) A wafer level packaged fully integrated hyperspectral Fabry-Perot filter with extended optical range, in: 2019 IEEE 32nd International Conference on Micro Electro Mechanical Systems (MEMS), 27–31 January 2019, Seoul, South Korea. IEEE. [Google Scholar]
- Geelen B., Tack N., Lambrechts A. (2014) A compact snapshot multispectral imager with a monolithically integrated per-pixel filter mosaic, in: von Freymann G., Schoenfeld W.V., Rumpf R.C. (eds), Advanced fabrication technologies for micro/nano optics and photonics VII, March 2014, San Francisco, CA, USA, SPIE. [Google Scholar]
- Hubold M., Karl J., Leitel R., Danz N., Brüning R. (2022) Concept, manufacturing and challenges of ultra-compact snapshot multi-spectral multi-aperture imaging systems, EPJ Web Conf. 266, 03013. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Hagen N., Dereniak E.L., Sass D.T. (2007) Fourier methods of improving reconstruction speed for CTIS imaging pectrometers, SPIE Proc. 6661, 666103-1–666103-11. [NASA ADS] [Google Scholar]
- Ahlebæk M.J., Peters M.S., Huang W.C., Frandsen M.T., Eriksen R.L., Jørgensen B. (2022) The hybrid approach – convolutional neural networks and expectation maximization algorithm – for tomographic reconstruction of hyperspectral images, J. Spectr. Imaging 12, Article ID a1. [Google Scholar]
- Huang W.C., Peters M.S., Ahlebaek M.J., Frandsen M.T., Eriksen R.L., Jørgensen B. (2021) The application of convolutional neural networks for tomographic reconstruction of hyperspectral images, Displays 74, 102218. [Google Scholar]
- Zimmermann M., Amann S., Mel M., Haist T., Gatto A. (2022) Deep learning-based hyperspectral image reconstruction from emulated and real computed tomography imaging spectrometer data, Opt. Eng. 61, 053103-1–053103-11. [NASA ADS] [CrossRef] [Google Scholar]
- Mel M., Gatto A., Zanuttigh P. (2022) Joint reconstruction and super resolution of hyper-spectral CTIS images, in: 33rd British Machine Vision Conference 2022, BMVC 2022, November 21–24, 2022, London, UK. BMVA Press. [Google Scholar]
- Narea-Jiménez F., Castro-Ramos J., Sánchez-Escobar J.J., Muñoz-Morales A. (2022) Assessment of a computed tomography imaging spectrometer using an optimized expectation-maximization algorithm, Appl. Opt. 61, 6076. [CrossRef] [Google Scholar]
- Peters M.S., Eriksen R.L., Jørgensen B. (2022) High-resolution snapshot hyperspectral computed tomography imaging spectrometer: Real-world applications, in: Georges M.P., Popescu G., Verrier N. (eds), Unconventional optical imaging III, 9–20 May, 2022, Strasbourg, France, SPIE. [Google Scholar]
- Habel R. (2017) 48–1: Invited paper: Spectral sensing with computed tomography imaging spectrometry, SID Symp. Digest Tech. Papers 48, 716. [CrossRef] [Google Scholar]
- Harvey J.E., Pfisterer R.N. (2019) Understanding diffraction grating behavior: Including conical diffraction and rayleigh anomalies from transmission gratings, Opt. Eng. 58, 087105-1–087105-21. [NASA ADS] [CrossRef] [Google Scholar]
- Seldowitz M.A., Allebach J.P., Sweeney D.W. (1987) Synthesis of digital holograms by direct binary search, Appl. Opt. 26, 2788. [NASA ADS] [CrossRef] [Google Scholar]
- Okamoto T., Yamaguchi I. (1991) Simultaneous acquisition of spectral image information, Opt. Lett. 16, 1277. [NASA ADS] [CrossRef] [Google Scholar]
- Volin C.E., Descour M.R., Dereniak E.L. (2002) Design of broadband-optimized computer-generated hologram dispersers for the computed-tomography imaging spectrometer, in: Descour M.R., Shen S.S. (eds), SPIE Proceedings, January 2002, San Diego, CA, USA, SPIE. [Google Scholar]
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