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Analysis of qualitative and quantitative assessment methods for shoreline extraction

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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
Strony
9--19
Opis fizyczny
Bibliogr. 52 poz., rys., tab.
Twórcy
  • Maritime University of Szczecin, Faculty of Navigation, Department of Geoinformatics
  • Maritime University of Szczecin, Chair of Geoinformatics, 46 Żołnierska St., 71-250 Szczecin, Poland
Bibliografia
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  • 25. Liu, Y., Wang, X., Ling, F., Xu, S. & Wang, C. (2017) Analysis of coastline extraction from Landsat-8 OLI imagery. Water 9(11), 816, doi: 10.3390/w9110816.
  • 26. Lubczonek, J. (2017) Application of Sentinel-1 imageries for shoreline extraction. IEEE 18th International Radar Symposium (IRS), doi: 10.23919/IRS.2017.8008161.
  • 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.
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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.
  • 52. Zhu, Z., Tang, Y., Hu, J. & An, M. (2019) Coastline Extraction from High-Resolution Multispectral Images by Integrating Prior Edge Information with Active Contour Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12(10), pp. 4099–4109, doi: 10.1109/JSTARS.2019.2939297.
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
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