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Volume 19, Issue 40 (11-2023)                   Marine Engineering 2023, 19(40): 1-8 | Back to browse issues page

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Hasani Moghaddam H, Ardini M, Bashiri A, Tabarsi H. Evaluation of hyper spectral imagery capabilities in seabed mapping. Marine Engineering 2023; 19 (40) :1-8
URL: http://marine-eng.ir/article-1-1027-en.html
1- MS Remote sensing researcher of Imam Hossein University, Tehran, Iran
2- MS Telecommunication, Shahed University, Tehran, Iran
3- Instructor of electronic Imam Hossein University, Tehran, Iran
4- MS Electronic Imam Hossein University, Tehran, Iran:
Abstract:   (1391 Views)
Due to their environmental and economic importance, water resources have always been considered by various researchers to study. One of the major problems in water resources studies is the deeper areas, which are not easily accessible. Underwater hyperspectral imaging(UHI) technologies have received a great deal of attention from various organizations and universities due to their ability to take images from deepwater resources. In this study, UHI hyperspectral images related to the depth of 3000 meters of the Pacific Ocean were used to prepare the seabed map. After applying the necessary pre-processing, the spectral resampling operation was performed. The spectra of 5 minerals and one artificial object were entered into the SAM algorithm for classification. The results of this study were presented as a map of the ocean floor, which shows the high ability of hyperspectral data to identify details related to the bed of water resources.
Full-Text [PDF 739 kb]   (343 Downloads)    
Type of Study: Research Paper | Subject: Environmental Study
Received: 2023/02/1 | Accepted: 2023/10/12

References
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]

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