Abstract Constant temporal and spatial monitoring of the coastline is essential for environmental protection. Waterlines were extracted from multi-source satellite remote sensing images, e.g., Landsat8, MODIS, HY-1C, and GF-3 SAR. A semi-automatic methodology of threshold segmentation was proposed to detect coastline based on local blocks of images, which is suitable for both optical and microwave remote sensing images and threshold scaling standards across different coastline types. The accuracy assessment of artificial, sandy, and muddy coastlines for each dataset was highly dependent on spatial resolution, and the result was GF-3 SAR. < Landsat8 < HY-1C < MODIS for RMSE and STD, and Landsat8 > HY-1C > MODIS > GF-3 SAR for accuracy within 1 pixel. For the angle of imaging effects of them, GF-3 SAR was inferior to other datasets on muddy coastline. The extraction effect of HY-1C was more closely correlated to the tide and its imaging effect of muddy coastline was the best among all datasets we selected. This work fused HY-1C images, which might have omitted some coastline information due to cloud interference, with GF-3 SAR images recorded over the same time period. The result showed that the fusion of optical and microwave remote sensing is effective and allows for better monitoring of the coastline.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Coastline detection using optical and synthetic aperture radar images


    Beteiligte:
    Yu, T. (Autor:in) / Xu, S.W. (Autor:in) / Tao, B.Y. (Autor:in) / Shao, W.Z. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-04-11


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Two Coastline Detection Methods Based on Improved Level Set Algorithm in Synthetic Aperture Radar Images

    Chong, J. / Ouyang, Y. / Zhu, M. | British Library Conference Proceedings | 2006


    Feature Detection in Synthetic Aperture Radar Images Using Fractal Error

    Jansing, E. D. / Chenoweth, D. L. / Knecht, J. et al. | British Library Conference Proceedings | 1997


    3-D terrain from synthetic aperture radar images

    Bors, A.G. / Hancock, E.R. / Wilson, R.C. | IEEE | 2000


    3-D Terrain from Synthetic Aperture Radar Images

    Bors, A. / Hancock, E. / Wilson, R. et al. | British Library Conference Proceedings | 2000


    Synthetic aperture radar

    Tema Archiv | 1976