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Abstrakty
The shoreline is an important geographical zone, and knowledge of its accurate location can be crucial for coastal management and mapping. The ever-increasing number of aerial and satellite sensors is leading to research related to the development of new methods for the automatic extraction of the shoreline. Currently, there is a lot of research in this area with different research methodologies. In this paper, an analysis of shoreline extraction methods was carried out. Based on the analysis undertaken, current research processes in this field can be verified. This enabled the further evaluation of the research methodologies studied, including the identification of basic assessment elements for shoreline extraction accuracy. Practical aspects of this work include the ability to establish the correct methods to assess the accuracy of extracted shorelines for both research and production processes related to data extracted from remotely sensed images.
Rocznik
Tom
Strony
9--19
Opis fizyczny
Bibliogr. 52 poz., rys., tab.
Twórcy
autor
- Maritime University of Szczecin, Faculty of Navigation, Department of Geoinformatics
autor
- Maritime University of Szczecin, Chair of Geoinformatics, 46 Żołnierska St., 71-250 Szczecin, Poland
Bibliografia
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- 2. Alicandro, M., Baiocchi, V., Brigante, R. & Radicioni, F. (2019) Automatic shoreline detection from eight-band VHR satellite imagery. Journal of Marine Science and Engineering, 7(12), 459, doi: 10.3390/jmse7120459.
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- 5. Bayram, B., Vãrna, I., Oğurlu, M., Bozkurt, S., Çatal, H. & Zafer Seker, D. (2015) Shoreline Extraction and Change Detection using 1:5000 Scale Orthophoto Maps: A Case Study of Latvia-Riga. International Journal of Environment and Geoinformatics 2(3), pp. 1–6, doi: 10.30897/ ijegeo.303552.
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- 27. Maglione, P., Parente, C. & Vallario, A. (2014) Coastline extraction using high resolution WorldView-2 satellite imagery. European Journal of Remote Sensing 47(1), pp. 685–699, doi: 10.5721/EuJRS20144739.
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- 31. Modava, M. & Akbarizadeh, G. (2017) Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method. International Journal of Remote Sensing 38(2), pp. 355–370, doi: 10.1080/01431161.2016.1266104.
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- 35. Pardo-Pascual, J.E., Almonacid-Caballer, J., Ruiz, L.A. & Palomar-Vázquez, J. (2012) Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision. Remote Sensing of Environment 123, pp. 1–11, doi: 10.1016/j.rse.2012.02.024.
- 36. Parente, C. & Santamaria, R. (2013) Increasing Geometric Resolution of Data Supplied by Quickbird Multispectral Sensors. Sensors and Transducers 156(9/13), pp. 111–115.
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- 38. Sekovski, I., Stecchi, F., Mancini, F. & Del Río, L. (2014) Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery. International Journal of Remote Sensing 35(10), pp. 3556–3578, doi: 10.1080/01431161.2014.907939.
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- 41. Templin, T., Popielarczyk, D. & Kosecki, R. (2018) Application of Low-Cost Fixed-Wing UAV for Inland Lakes Shoreline Investigation. Pure and Applied Geophysics 175(9), pp. 3263–3283, doi: 10.1007/s00024-017-1707-7.
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- 51. Zhang, T., Yang, X., Hu, S. & Su, F. (2013) Extraction of coastline in aquaculture coast from multispectral remote sensing images: Object-based region growing integrating edge detection. Remote Sensing 5(9), pp. 4470–4487, doi: 10.3390/rs5094470.
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a0a4e76f-f8c2-409e-a10b-2af46fe5d784