1. 1- Yahya, N. N., Hashim, M., & Ahmad, S., (2014), Remote Sensing of shallow sea floor for digital earth environment, IOP Conference Series: Earth and Environmental Science, 18, 012110 [
DOI:10.1088/1755-1315/18/1/012110]
2. Ji, F., Pawlowicz, R., & Xiong, X., (2021), Estimating the Absolute Salinity of Chinese offshore waters using nutrients and inorganic carbon data, Ocean Science, Vol.17, p. 909-918 [
DOI:10.5194/os-17-909-2021]
3. Daniel, A., Laës-Huon, A., Barus, C., Beaton, A. D., Blandfort, D., Guigues, N., … Achterberg, E. P., (2020), Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment. Frontiers in Marine Science, Vol.6, p.1-22 [
DOI:10.3389/fmars.2019.00773]
4. Mullen, L., O'Connor, S., Cochenour, B., & Dalgleish, F., (2013), State-of-the-art tools for next-generation underwater optical imaging systems, Ocean Sensing and Monitoring, Vol.5, p.661-684 [
DOI:10.1117/12.2018489]
5. Raizer, V., (2019), Optical Remote Sensing Technologies. Optical Remote Sensing of Ocean Hydrodynamics, p.133-150. [
DOI:10.1201/9781351119184-4]
6. Mikelsons, K., Wang, M., & Jiang, L., (2020), Statistical evaluation of satellite ocean color data retrievals, Remote Sensing of Environment, Vol. 237 [
DOI:10.1016/j.rse.2019.111601]
7. Yan, Q., (2020), Advantage and Application of Unmanned Aerial Vehicle Remote Sensing in Engineering Survey, Remote Sensing, Vol.9, p.1-22 [
DOI:10.18282/rs.v9i1.1098]
8. Sture, O., Ludvigsen, M., Soreide, F., & Aas, L. M. S., (2017), Autonomous underwater vehicles as a platform for underwater hyperspectral imaging, OCEANS, Vol.2017, p.1-8 [
DOI:10.1109/OCEANSE.2017.8084995]
9. Cunningham, A., & Mckee, D., (2013), Measurement of hyperspectral underwater light fields, Subsea Optics and Imaging, Vol.2013, p.83-97 [
DOI:10.1533/9780857093523.2.83]
10. Liu, B., Liu, Z., Men, S., Li, Y., Ding, Z., He, J., & Zhao, Z., (2020), Underwater Hyperspectral Imaging Technology and Its Applications for Detecting and Mapping the Seafloor: A Review, Sensors, Vol.20, p.1-21 [
DOI:10.3390/s20174962] [
PMID] [
]
11. Jin, X., Li, Z., Feng, H., Ren, Z., & Li, S., (2020), Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model. Agricultural Water Management, Vol.227 [
DOI:10.1016/j.agwat.2019.105846]
12. Wei, H., Guo, Y., Yang, P., Song, H., Liu, H., & Zhang, Y., (2017), Underwater multispectral imaging: The influences of color filters on the estimation of underwater light attenuation, OCEANS, Vol.2017 [
DOI:10.1109/OCEANSE.2017.8084894]
13. Wang, S., Chi, C., Wang, P., Liu, J., & Huang, H., (2020), Design of a low-complexity miniature underwater three-dimensional acoustical imaging system, International Conference on Underwater Acoustics [
DOI:10.1121/2.0001317]
14. Yamamoto, S., Kato, K., & Abe, S., (2020), Optical imaging of produced light in water during irradiation of gamma photons lower energy than the Cerenkov-light threshold, Applied Radiation and Isotopes, Vol.15 [
DOI:10.1016/j.apradiso.2020.109037] [
PMID]
15. Kralikova, R., Badida, M., & Konkoly, T., (2015), Lighting Quality and Visual Comfort Assesment in Working Environment, Proceedings of the 21st International Conference LIGHT SVĚTLO 2015 [
DOI:10.13164/conf.light.2015.109]
16. Salisbury, A., & Matthews, A, (2020), Using airborne hyperspectral imaging to aid prospectivity analysis for lithium in geothermal waters, Hyperspectral Imaging and Applications, Vol.11576 [
DOI:10.1117/12.2583968] [
PMID] [
]
17. Johnsen, G., Ludvigsen, M., Sørensen, A., & Sandvik Aas, L. M., (2016), The use of underwater hyperspectral imaging deployed on remotely operated vehicles - methods and applications, IFAC-PapersOnLine, Vol.49, p.476-481 [
DOI:10.1016/j.ifacol.2016.10.451]
18. Kjerstad.I., (2014), Underwater imaging and the effect of inherent optical properties on image quality, MSc thesis of NTNU University of Norway
19. FORESTI, G. L., & GENTILI, S., (2000), A VISION BASED SYSTEM FOR OBJECT DETECTION IN UNDERWATER IMAGES, International Journal of Pattern Recognition and Artificial Intelligence, Vol.14 p.167-188 [
DOI:10.1142/S021800140000012X]
20. Deva Krupa. A.J, Samiappan.D, Hemalatha.V., (2018), Techniques for seabed mapping usin underwater hyperspectral imaging: A survey, Pure and applied mathematics. Vol.118, p.11-30
21. Naik, M., (2017), Evolution of Sonar Survey Systems for Sea Floor Studies, Engineering and Technology Journal, Vol.2, p.185-195. [
DOI:10.18535/etj/v2i6.01]
22. Wilson, S., Potgieter, J., & Arif, K. M., (2019), Robot-Assisted Floor Surface Profiling Using Low-Cost Sensors. Remote Sensing, Vol.11, p.1-25 [
DOI:10.3390/rs11222626]
23. Xiong, F., Zhou, J., Chanussot, J., & Qian, Y., (2019), Dynamic Material-Aware Object Tracking in Hyperspectral Videos, 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) [
DOI:10.1109/WHISPERS.2019.8921176]
24. Matouskova, E., (2014), INFLUENCE OF ILLUMINATION AND WHITE REFERENCE MATERIAL FOR HYPERSPECTRAL IMAGING OF CULTURAL HERITAGE OBJECTS, 14th SGEM Geo Conference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING [
DOI:10.5593/SGEM2014/B23/S10.025]
25. Rafert, J., (2015), Advances in hyperspectral remote sensing I: The visible Fourier transform hyperspectral imager, Journal of Spectral Imaging, Vol.4, p.1-5 [
DOI:10.1255/jsi.2015.a1]
26. Buscombe, D., (2017), Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar, Environmental Modelling & Software, Vol.89, p.1-18 [
DOI:10.1016/j.envsoft.2016.12.003]
27. Liu, X., Sun, C., Yang, Y., & Zhuo, J., (2017), Hybrid phase shift and shifted sideband beamforming for large‐aperture MIMO sonar imaging, IET Radar, Sonar & Navigation, Vol.11, p.1782-1789 [
DOI:10.1049/iet-rsn.2016.0557]
28. Prokhorov, I. V., & Sushchenko, A. A., (2015), Imaging Based on Signal from Side-Scan Sonar, Applied Mechanics and Materials, Vol.756, p.678-682 [
DOI:10.4028/www.scientific.net/AMM.756.678]
29. Chowdhury, S., Zhang, J., Staenz, K., & Peddle, D., (2012), Spectral mixture analysis of hyperspectral data using Genetic Algorithm and Spectral Angle Constraint (GA-SAC), 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS [
DOI:10.1109/WHISPERS.2012.6874227] [
]
30. Hasani Moghaddam, H., Torahi, Ali Asghar., & Zeaiean Firooz Abadi, P., (2019), Using discrete wavelet transform to increase the accuracy of hyper spectral and high resolution images fusion, JRORS, Vol.1(2019), p.22-30
31. Kala, S., & Vasuki, S., (2014), Feature correlation based parallel hyper spectral image compression using a hybridization of FCM and subtractive clustering, Journal of Communications Technology and Electronics, Vol.59, p.1378-1389 [
DOI:10.1134/S1064226914120195]
32. Schaefli, B., & Kavetski, D., (2017), Bayesian spectral likelihood for hydrological parameter inference, Water Resources Research, Vol.53, p.6857-6884 [
DOI:10.1002/2016WR019465]