1. Golovin, A.D., Dyomin, A.V. The Imitation Model of the Multi-channel Offner Hyperspectrometer [Imitatsionnaya model mnogokanalnogo giperspectrometra Offnera] // Kompyuternaya optika, 2015, Vol. 39, # 4, pp. 521–528. Doi 10.18287/0134–2452–2015–39–4–521–528.
2. Larar Allen M. et al. Multispectral, hyperspectral, and ultraspectral remote sensing technology, techniques, and applications III // Proc. SPIE International Society for Optical Engineering. 13–14 October 2010. Incheon, Korea Republic, 2010, Vol. 7857.
3. Rodionov, I.D., Rodionov, A.I., Vedeshin, L.A., Vinogradov, A.N., Egorov, V.V., Kalinin, A.P. Aviation Hyperspectral Installations for Remote Sensing Applications [Aviatsionnyie giperspektralnyie kompleksy dlya resheniya zadach distantsionnogo zondirovaniya] // Issledovaniye zemli iz kosmosa, 2013, Vol. 6, pp. 81–93.
4. Gorbunov, G.G., Chikov, K.N., Shlishevskiy, V.B. Interferential Hyper and Ultraspectral Videospectrometers for Remote Sensing Applications [Interferentsionnyie giper- i ultraspektralnyie videospektrometry dlya zadach distantsionnogo zondirovaniya] // Bulleting of SGUGiT, 2016, Vol. 1, # 33, pp. 70–94.
5. Vilaseca M., Schael B., Delpueyo X., Chorro E., Perales E., Hirvonen T., Pujol J. Repeatability, reproducibility, and accuracy of a novel pushbroom hyperspectral system // Color Research & Application, 2013, Vol. 39, No. 6, pp. 549–558. Doi 10.1002/col.21851.
6. Pozhar, V.E., Machikhin, A.S., Gaponov, M.I., Shirokov, S.V., Mazur, M.M., Sheryshev, A.E. Hyper-spectrometer Based on an Acousto-optic Tuneable Filters for UAVS // Light & Engineering, 2019, Vol. 27, # 3, pp. 99–104.
7. Palto, S.P., Alpatova, A.V., Geyvandov, A.R., Blinov, L.M., Lazarev, V.V., Yudin, S.G. Fourier Spectroscopy as a Method of Studying of Photoelectric Properties of Organic Systems [Furye-spektroskopiya kak metod izucheniya fotoelektricheskikh svoistv organicheskikh sistem] // Optika i spektroskopiya, 2018, Vol. 124, # 2, pp. 210–220. Doi 10.21883/OS.2018.02.45526.209–17.
8. Zavarzin, V.I., Mitrofanova, Yu.S. Schematic Solutions for Advanced Hyperspectral Systems [Skhemnyie resheniya dlya perspektivnoy giperspektralnoy apparatury] // Opticheskiy zhurnal, 2017, Vol. 84, # 4, pp. 12–16.
9. Golovin, A.D., Dyomin, A.V. The Imitation Model of the Multi-channel Offner Hyperspectrometer [Imitatsionnaya model mnogokanalnogo giperspectrometra Offnera] // Kompyuternaya optika, 2015, Vol. 39, # 4, pp. 521–527. Doi 10.18287/0134–2452–2015–39–4–521–528.
10. Balashov, A.A., Vagin, V.A., Golyak, I.S., Morozov, A.N., Nesteruk, I.N., Khorokhorin, A.I. Visible and Near-IR Range Fourier Spectrometer [Furye-spektrometr vidimogo i blizhnego IK diapazonov] // Radiostroeniye, 2017, Vol. 6, pp. 27–38. Doi 10.24108/rdeng.0617.0000124.
11. Arkhipov, S.A., Zavarzin, V.I., Lee, A.V. // Reflective Optical Systems for Low-dimension Hyperspectral Systems of Remote Sensing of Earth [Zerkalnyie opticheskiye sistemy dlya malogabaritnoy giperspektralnoy apparatury distantsionnogo zondirovaniya zemli iz kosmosa] // From the collection Acoustic-optic and Radar Methods of Measurement and Information Processing. Proceedings of the 10th International Science and Technical Conference. A.S. Popov Russian Society of Radio Engineering, Electronics and Communication (NTORES), 2017, pp. 262–264.
12. Veys C., Davies P., Hibbert J., Grieve B. (2017). An Ultra-Low-Cost Active Multispectral Crop Diagnostics Device. 1005–1007. Paper presented at IEEE Sensors 2017 Conference, Glasgow, United Kingdom. Doi.org/10.1109/ICSENS.2017.8234211.
13. Burns P.D., Berns R.S. Analysis Multispectral Image Capture // Color Imaging Conferenct 1996, # 1, pp. 19–22.
14. Khorokhorov, A.M., Vvedenskaya, A.V., Shirankov, A.F., Kobozev, V.S., Algorithm of Enhancement of Matrix Radiation Detectors with Bayer filters [Algoritm rasshireniya vozmozhnostey matrichnykh priyomnikov izlucheniya s filtrami Bayera / Int. Conf. Applied Optics 2018, Saint Petersburg: D.S. Rozhdestvensky Society of Optics, 2018, Vol. 2.
15. Varentsova, S.A., Trofimova, V.A., Troshchiyev, Yu.V. Reconstruction of a Signal and Dynamics of its Spectral Characteristics with a Non-regular Set of Measurements [Vosstanovleniye signala i dinamiki ego spektralnykh kharakteristik pri neregulyarnom nabore izmereniy] // J. tech. phys. 2008, Vol. 78, # 7, pp. 57–68.
16. Zhuchko, O.V., Pytyev, Yu.P. Reconstruction of Functional Dependence Using Theoretical and Possibility Methods // J. comp. math. and math. phys. 2003, Vol. 43, # 5, pp. 767–783.
17. Hardeberg J. Acquisition and reproduction of color images: colorimetric and multispectral approaches. USA: Dissertation.com, 2001.
18. Vapnik, V.N. Reconstruction of Dependences Using Empirical Data [Vosstanovleniye zavisimostey po empiricheskim dannym], Moscow: Nauka, 1979, 448 p.
19. Ivanov, V.K., Vasin, V.V., Tanana, V.P. The Theory of Linear Incorrect Problems and its Application [Teoriya lineynykh nekorrektnykh zadach i eyo prilozhenie], Moscow: Nauka, 1978, 206 p.
20. Sizikov, V.S., Lavrov, A.V. Contemporary Sustainable Mathematical and Software Methods of Distorted Spectra Reconstruction [Sovremennyie ustoichivyie matematicheskiye i programmnyie metody vosstanovleniya iskazhonnykh spektrov] // Science and Technical Bulletin of Information Technologies, Mechanics and Optics, 2018, Vol. 18, # 6.
21. Sydikhov, A. Sh., Arapov, S. Yu., Arapova, S.P. Pseudoinverse Processing of the Data of Multispectral Shooting in Stationary Zones of an Image [Psevdoinversnaya obrabotka dannykh multispektralnoy fotosyomki v statsionarnykh zonakh izobrazheniya] / Int. Conf. of Students, Postgraduates and Young Scientists Information Technologies, Telecommunications and Control Systems: book of reports, Ekaterinburg: UrFU, 2015, pp. 179–185.
22. El-Rifai I. et al. Enhanced Spectral Reflectance Reconstruction Using Pseudo-Inverse Estimation Method // Int. J. Image Process. IJIP, 2013, Vol. 7, # 3, pp. 278–285.
23. Barliani, A.G. Development of Algorithms of Equalisation and Evaluation of Accuracy of Unconstrained and Constrained Geodetic Networks Based on a Pseudonormal Solution [Razrabotka algoritmov uravnivaniya i otsenki tochnosti svobodnykh i nesvobodnykh geodezicheskikh setey na osnove psevdonormalnogo resheniya, Novosibirsk: SGGA, 2010, 135 p.
24. Morozov, V.V., Grebennikov, A.I. Methods of Solution of Incorrectly Set Problems. The Algorithm Aspect [Metody resheniya nekorrektno postavlennykh zadach. Algoritmicheskiy aspekt]. – Moscow: Moscow University Press, 1992, 319 p.
25. Vogel C.R. Computational Methods for Inverse Problems. SIAM. Philadelphia, 2002, 179 p. Doi 10.1137/1.9780898717570.
26. Gurylyova, A.V., Khorokhorov, A.M., Latyshev V.I. Comparative Analysis of Methods of Solving Incorrect Reverse Problems for Multi-channel Hyperspectroscopy [Sravnitelnyi analiz metodov resheniya nekorrektnykh obratnykh zadach dlya mnogokanalnoy giperspektrometrii] // Optika i spektroskopiya, 2019, Vol. 127, # 4, pp. 551–557. Doi 10.21883/OS.2019.10.48356.171–19.
27. Arapov, S. Yu., Arapova, S.P., Dubinin, I.S., Sergeev, A.P. Reconstruction of Reflection Spectra of Test Fields Based on Data of Multispectral Shooting [Vosstanovleniye spektrov otrazheniya testovykh poley po dannym multispektralnoy fotosyomki] / Transmission, Processing, Perception of Text and Graphic Information: proceedings of the International Science and Practical Conference. – Ekaterinburg: UrFU, 2015. – P. 21–33.
28. Arapov, S. Yu., Tarasov, D.A., Sergeev, A.P., Kolmogorov, Yu.N. Modelling of Reflection Spectra Using the Basis of Erf Functions [Modelirovaniye spektrov otrazhaniya na osnove bazisa iz funktsiy tipa integrala oshibok] // Izvestiya vysshykh uchebnykh zavedeniy. Problemy poligrafii i izdatelskogo dela, 2012, Vol. 6, pp. 17–29.
29. Novikov, L.V. Spectral Analysis of Signals in Wavelet Basis [Spektralnyi analiz signalov v bazise veivletov] // Nauchnoye priborostroeniye. 2000, Vol. 10, # 3, pp. 70–77.
30. Tarasov, D.A. Modelling of Reflection Spectra by Polynome Superposition [Modelirovaniye spektrov otrazheniya superpozitsiey polinomov] // Izvestiya vysshykh uchebnykh zavedeniy. Problemy poligrafii i izdatelskogo dela, 2012, Vol. 5, pp. 59–66.
31. Tikhonov, A.N., Arsenin, V. Ya. Methods of Solving of Incorrect Problems [Metody resheniya nekorrektnykh zadach]. Moscow: Nauka, 1986, 288 p.
32. Calvetti, D., Morigi, S., Reichel, L., Sgallari, F. Tikhonov regularization and the L-curve for large discrete ill-posed problems // J. Comput. Appl. Math. 2000, Vol. 123, pp. 423–446.
33. Hansen, C.A. Matlab Package for Analysis and Solution of Discrete Ill-Posed Problems // Numerical Algorithms, 1994, Vol. 6, pp. 1–35.
34. Morozov, V.A. Linear and Non-linear Incorrect Problems [Lineynyie i nelineynyie nekorrektnyie zadachi] // Itogi nauki i tekhniki. Ser. Math. anal. 1973, Vol. 11, pp. 129–178.
35. Goncharskiy, A.V., Leonov, A.V., Yagola, A.G. Generalised Residual Principle [Printsip obobshchyonnoy nevyazki] // J. Comp. Math. & Math. Phys. 1973, Vol. 13, # 2, pp. 294–302.
36. Godunov, S.K., Antonov, A.G., Kirilyuk, O.P., Kostin, V.I. Guaranteed Accuracy of Solution of a System of Linear Equations in Euclidean Spaces [Garantirovannaya tochnost resheniya sistem lineynykh uravneniy v evklidovykh prostranstvakh]. Novosibirsk: Nauka: Sib. branch, 1988, 456 p.
37. Vapnik, V.N., Mikhalskiy, A.I. On Searching of Dependences Using the Method of Ordered Risk Minimisation [O poiske zavisimostey metodom uporyadochennoy minimizatsii riska] // Automat. & telemech. 1974, Vol. 10, pp. 10–21.
38. Connah, D., Alsam, A., Hardeberg, J.Y. Multispectral imaging: How many sensors do we need? // J. Imaging Sci. Technol. 2006, Vol. 50, #. 1, pp. 45–52. Doi 10.2352/j.imagingsci.technol. (2006)50:1(45).
39. Helling, S., Seidel, E., Biehlig, W. Algorithms for spectral color stimulus reconstruction with a seven-channel multispectral camera / Proc. of CGIV (2nd European Conference on Color in Graphics, Imaging and Vision). 2004, Vol. 2, pp. 254–258.
40. Imai, F.H., Berns, R.S. Spectral estimation using trichromatic digital cameras // Proc. Int. Symp. Multispectral Imaging and Color Reproduction for Digital Archives. Chiba: Society of Multispectral Imaging of Japan, 1999, pp. 42–48.
41. Masahiro, Y., Hideaki, H., Nagaaki, O. Beyond Red–Green–Blue (RGB): Spectrum-Based Colour Imaging Technology // J. Imaging Sci. Technol. 2008, Vol. 52, #. 1, pp. 1–15.
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