A Reduction Method for Bathymetric Datasets that Preserves True Coastal Water Geodata - Publikacja - MOST Wiedzy

Wyszukiwarka

A Reduction Method for Bathymetric Datasets that Preserves True Coastal Water Geodata

Abstrakt

Water areas occupy over 70 percent of the Earth’s surface and are constantly subject to research and analysis. Often, hydrographic remote sensors are used for such research, which allow for the collection of information on the shape of the water area bottom and the objects located on it. Information about the quality and reliability of the depth data is important, especially during coastal modelling. In-shore areas are liable to continuous transformations and they must be monitored and analyzed. Presently, bathymetric geodata are usually collected via modern hydrographic systems and comprise very large data point sequences that must then be connected using long and laborious processing sequences including reduction. As existing bathymetric data reduction methods utilize interpolated values, there is a clear requirement to search for new solutions. Considering the accuracy of bathymetric maps, a new method is presented here that allows real geodata to be maintained, specifically position and depth. This study presents a description of a developed method for reducing geodata while maintaining true survey values.

Cytowania

  • 1

    CrossRef

  • 1

    Web of Science

  • 1

    Scopus

Autorzy (3)

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
Remote Sensing nr 11, strony 1 - 21,
ISSN: 2072-4292
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
Wlodarczyk-Sielicka M., Stateczny A., Lubczonek J.: A Reduction Method for Bathymetric Datasets that Preserves True Coastal Water Geodata// Remote Sensing. -Vol. 11, iss. 13 (2019), s.1-21
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs11131610
Bibliografia: test
  1. Brown, M.E.; Kraus, N.C. Tips for Developing Bathymetry Grids for Coastal Modeling System Applications; Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory: Vicksburg, MS, USA, 2007.
  2. Mishra, P.; Panda, U.S.; Pradhan, U.C.; Kumar, S.; Naik, S.; Begum, M.; Ishwarya, J. Coastal Water Quality Monitoring and Modelling Off Chennai City. Procedia Eng. 2015, 116, 955-962. [CrossRef] otwiera się w nowej karcie
  3. Stansby, P.K. Coastal hydrodynamics-Present and future. J. Hydraul. Res. 2014, 51, 341-350. [CrossRef] otwiera się w nowej karcie
  4. Bottelier, P.; Haagmans, R.; Kinneging, N. Fast Reduction of High Density Multibeam Echosounder Data for Near Real-Time Applications. Hydrogr. J. 2000, 98, 23-28.
  5. Remote Sens. 2019, 11, 1610 otwiera się w nowej karcie
  6. Burroughes, J.; George, K.; Abbot, V. Interpolation of hydrographic survey data. Hydrogr. J. 2001, 99, 21-23.
  7. Hansen, R.E.; Callow, H.J.; Sabo, T.O.; Synnes, S.A.V. Challenges in Seafloor Imaging and Mapping with Synthetic Aperture Sonar. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3677-3687. [CrossRef] otwiera się w nowej karcie
  8. Jong, C.D.; Lachapelle, G.; Skone, S.; Elema, I.A. Hydrography, 2nd ed.; DUP Blue Print: Delft, The Netherlands, 2010.
  9. Moszynski, M.; Chybicki, A.; Kulawiak, M.; Lubniewski, Z. A novel method for archiving multibeam sonar data with emphasis on efficient record size reduction and storage. Pol. Marit. Res. 2013, 20, 77-86. [CrossRef] otwiera się w nowej karcie
  10. Rezvani, M.; Hadi, S.; Abbas, A.; Alireza, A. Robust Automatic Reduction of Multibeam Bathymetric Data Based on M-estimators. Mar. Geod. 2015, 38, 327-344. [CrossRef] otwiera się w nowej karcie
  11. Calder, B.R.; Mayer, L.A. Automatic processing of high-rate, high-density multibeam echosounder data. Geochem. Geophys. Geosyst. 2003, 4, 1048. [CrossRef] otwiera się w nowej karcie
  12. Yang, F.; Li, J.; Han, L.; Liu, Z. The filtering and compressing of outer beams to multibeam bathymetric data. Mar. Geophys. Res. 2013, 34, 17-24. [CrossRef] otwiera się w nowej karcie
  13. Kazimierski, W.; Wlodarczyk-Sielicka, M. Technology of Spatial Data Geometrical Simplification in Maritime Mobile Information System for Coastal Waters. Pol. Marit. Res. 2016, 23, 3-12. [CrossRef] otwiera się w nowej karcie
  14. Specht, C.; Switalski, E.; Specht, M. Application of an Autonomous/Unmanned Survey Vessel (ASV/USV) in bathymetric measurements. Pol. Marit. Res. 2017, 24, 36-44. [CrossRef] otwiera się w nowej karcie
  15. Kulawik, M.; Lubniewski, Z. Processing of LiDAR and multibeam sonar point cloud data for 3D surface and object shape reconstruction. In Proceedings of the 2016 Baltic Geodetic Congress (BGC Geomatics), Gdańsk, Poland, 2-4 June 2016. [CrossRef] otwiera się w nowej karcie
  16. Blaszczak-Bak, W. New Optimum Dataset method in LiDAR processing. Acta Geodyn. Geomater. 2016, 13, 379-386. [CrossRef] otwiera się w nowej karcie
  17. Blaszczak-Bak, W.; Sobieraj-Zlobinska, A.; Kowalik, M. The OptD-multi method in LiDAR processing. Meas. Sci. Technol. 2017, 28, 075009. [CrossRef] otwiera się w nowej karcie
  18. Blaszczak-Bak, W.; Koppanyi, Z.; Toth, C. Reduction Method for Mobile Laser Scanning Data. ISPRS Int. J. Geo Inf. 2018, 7, 285. [CrossRef] otwiera się w nowej karcie
  19. Holland, M.; Hoggarth, A.; Nicholson, J. Hydrographic processing considerations in the "Big Data" age: An overview of technology trends in ocean and coastal surveys. Earth Environ. Sci. 2016, 34, 012016. [CrossRef] otwiera się w nowej karcie
  20. International Hydrographic Organization (IHO). Transfer Standard for Digital Hydrographic Data, 3rd ed.; Special Publication No. 57; International Hydrographic Organization: Monte Carlo, Monaco, 2002. 20. International Hydrographic Organization (IHO). Standards for Hydrographic Surveys, 5th ed.; Special Publication No. 44; International Hydrographic Organization: Monte Carlo, Monaco, 2008. otwiera się w nowej karcie
  21. Wlodarczyk-Sielicka, M.; Stateczny, A. Clustering bathymetric data for electronic navigational charts. J. Navig. 2016, 69, 1143-1153. [CrossRef] otwiera się w nowej karcie
  22. Lenk, U.; Kruse, I. Multibeam data processing. Hydrogr. J. 2001, 102, 9-14.
  23. Maleika, W.; Palczynski, M.; Frejlichowski, D. Interpolation Methods and the Accuracy of Bathymetric Seabed Models Based on Multibeam Echosounder Data. Lect. Notes Artif. Intell. 2012, 7198, 466-475. otwiera się w nowej karcie
  24. Cao, J.; Cui, H.; Shi, H.; Jiao, L. Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce. PLoS ONE 2016, 11, e0157551. [CrossRef] otwiera się w nowej karcie
  25. Liu, S.; Wang, L.; Liu, H.; Su, H.; Li, X.; Zheng, W. Deriving Bathymetry from Optical Images with a Localized Neural Network Algorithm. IEEE Trans. Geosci. Remote Sens. 2018, 56, 5334-5342. [CrossRef] otwiera się w nowej karcie
  26. Lubczonek, J. Hybrid neural model of the sea bottom surface, Artificial Intelligence and Soft Computing-ICAISC. Lect. Notes Comput. Sci. 2004, 3070, 1154-1160. otwiera się w nowej karcie
  27. Sibaja-Cordero, J.A.; Troncoso, J.S.; Benavides-Varela, C.; Cortés, J. Distribution of shallow water soft and hard bottom seabeds in the Isla del Coco National Park, Pacific Costa Rica. Rev. Biol. Trop. 2012, 60, 53-66. otwiera się w nowej karcie
  28. Kogut, T.; Niemeyer, J.; Bujakiewicz, A. Neural networks for the generation of sea bed models using airborne lidar bathymetry data. Geod. Cartogr. 2016, 65, 41-53. [CrossRef] otwiera się w nowej karcie
  29. Huang, S.Y.; Liu, C.L.; Ren, H. Costal Bathymetry Estimation from Multispectral Image with Back Propagation Neural Network. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, 41, 1123-1125. [CrossRef] otwiera się w nowej karcie
  30. Li, Z. Algorithmic Foundation of Multi-Scale Spatial Representation; CRC Press: Boca Raton, FL, USA, 2007. otwiera się w nowej karcie
  31. Habib ur Rehman, M.; Chang, V.; Batool, A.; Wah, T.Y. Big data reduction framework for value creation in sustainable enterprises. Int. J. Inf. Manag. 2016, 36, 917-928. [CrossRef] otwiera się w nowej karcie
  32. Remote Sens. 2019, 11, 1610 21 of 21 otwiera się w nowej karcie
  33. Habib ur Rehman, M.; Jayaraman, P.; Malik, S.; Khan, A.; Medhat Gaber, M. RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments. J. Sens. Actuator Netw. 2017, 6, 17. [CrossRef] otwiera się w nowej karcie
  34. Zhong, R.Y.; Newman, S.T.; Huang, G.Q.; Lan, S. Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Comput. Ind. Eng. 2016, 101, 572-591. [CrossRef] otwiera się w nowej karcie
  35. Aykut, N.O.; Akpinar, B.; Aydin, O. Hydrographic data modeling methods for determining precise seafloor topography. Comput. Geosci. 2013, 17, 661-669. [CrossRef] otwiera się w nowej karcie
  36. Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 1982, 43, 59-69. [CrossRef] otwiera się w nowej karcie
  37. Kohonen, T. The self-organizing map. Proc. IEEE 1990, 78, 1464-1480. [CrossRef] otwiera się w nowej karcie
  38. Tang, X. Fuzzy clustering based self-organizing neural network for real time evaluation of wind music. Cogn. Syst. Res. 2018, 52, 359-364. [CrossRef] otwiera się w nowej karcie
  39. Osowski, S. Artificial Neural Networks for Information Processing; Warsaw University of Technology Publishing House: Warszawa, Poland, 2000. (In Polish)
  40. Wlodarczyk-Sielicka, M.; Lubczonek, J.; Stateczny, A. Comparison of Selected Clustering Algorithms of Raw Data Obtained by Interferometric Methods Using Artificial Neural Networks. In Proceedings of the 2016 17th International Radar Symposium (IRS), Krakow, Poland, 10-12 May 2016. [CrossRef] otwiera się w nowej karcie
  41. Wlodarczyk-Sielicka, M. Importance of neighborhood parameters during clustering of bathymetric data using neural network. In International Conference on Information and Software Technologies; otwiera się w nowej karcie
  42. Dregvaite, G., Damasevicius, R., Eds.; Springer: Cham, Switzerland, 2016; pp. 441-452. [CrossRef] otwiera się w nowej karcie
  43. Wlodarczyk-Sielicka, M.; Stateczny, A. Selection of SOM Parameters for the Needs of Clusterisation of Data Obtained by Interferometric Methods. In Proceedings of the 2015 16th International Radar Symposium (IRS), Dresden, Germany, 24-26 June 2015; pp. 1129-1134. [CrossRef] otwiera się w nowej karcie
  44. Wlodarczyk-Sielicka, M.; Lubczonek, J. The Use of an Artificial Neural Network to Process Hydrographic Big Data during Surface Modeling. Computers 2019, 8, 26. [CrossRef] otwiera się w nowej karcie
  45. Caris, Bathy DataBASE Manager/Editor Reference Guide, 2011.
Weryfikacja:
Politechnika Gdańska

wyświetlono 71 razy

Publikacje, które mogą cię zainteresować

Meta Tagi