Open Access
E3S Web Conf.
Volume 310, 2021
Annual International Scientific Conference “Spatial Data: Science, Research and Technology 2021”
Article Number 04002
Number of page(s) 15
Section Aerophotosurveying. Photogrammetrics
Published online 15 October 2021
  1. A. Puissant, S. Lefèvre, J. Weber, Coastline extraction in VHR imagery using mathematical morphology with spatial and spectral knowledge, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, Part B8, pp. 1305-1310, Beijing, China (2008) [Google Scholar]
  2. E. Boak, I. Turner, Shoreline Definition and Detection: A Review, J. of Coastal Research, 21, pp. 688-703, (2005) [Google Scholar]
  3. V. Baiocchi, R. Brigante, D. Dominici, F. Radicioni, Coastline Detection Using High Resolution Multispectral Satellite Images, FIG Working Week 2012, 6-10 May 2012, Rome, Italy, (2012) [Google Scholar]
  4. P. Maglione, C. Parente, A. Vallario, Coastline extraction using high resolution WorldView-2 satellite imagery, European Journal of Remote Sensing, 47:1, pp. 685699, (2014) [Google Scholar]
  5. K. Smith, J. Terrano, J. Pitchford, M. Archer, Coastal Wetland Shoreline Change Monitoring: A Comparison of Shorelines from High-Resolution WorldView Satellite Imagery, Aerial Imagery, and Field Surveys, Remote Sensing, 13, 3030, (2021) [Google Scholar]
  6. J. Gu, Zh. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, T. Chen, Recent advances in convolutional neural networks, Pattern Recognition, 77, pp. 354-377, (2018) [Google Scholar]
  7. A. Krizhevsky, I. Sutskever, G. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Communications of the ACM, 60, 6, pp. 84-90, (2017) [Google Scholar]
  8. K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, (2016) [Google Scholar]
  9. O. Ronneberger, P. Fischer, T. Brox Thomas, U-Net: Convolutional Networks for Biomedical Image Segmentation, Lecture Notes in Computer Science, 9351, pp. 234241, (2015) [Google Scholar]
  10. R. Li, W. Liu, L. Yang, S. Sun, W. Hu, F. Zhang, W. Li, DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 11, pp. 3954-3962, (2018) [Google Scholar]
  11. Z. Chu, T. Tian, R. Feng, L. Wang, Sea-Land Segmentation With Res-UNet And Fully Connected CRF, 2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 3840-3843, (2019) [Google Scholar]
  12. M. A. Ridwan, N.A.M. Radzi, W.S.H.M.W. Ahmad, I.S. Mustafa, N.M. Din, Y.E. Jalil, A.M. Isa, N.S. Othman, W.M.D.W. Zakiet, Applications of Landsat-8 Data: a Survey, International Journal of Engineering & Technology, 7, 4.35, pp. 436-441, (2018) [Google Scholar]
  13. M. Besset, N. Gratiot, E. J. Anthony, F. Bouchette, M. Goichot, P. Marchesiello, Mangroves and shoreline erosion in the Mekong River delta, Viet Nam, Estuarine, Coastal and Shelf Science, 226, 106263, (2019) [Google Scholar]
  14. B. K. Veettil, X. Q. Ngo, T. T. T. Ngo, Changes in mangrove vegetation, aquaculture and paddy cultivation in the Mekong Delta: A study from Ben Tre Province, southern Vietnam, Estuarine, Coastal and Shelf Science, 226, 106273, (2019) [Google Scholar]
  15. K. R. Olson, L. W. Morton, Polders, dikes, canals, rice, and aquaculture in the Mekong Delta, Journal of Soil and Water Conservation, 73, 4, (2018) [Google Scholar]
  16. T. D. Truong, L. H. Do, Mangrove forests and aquaculture in the Mekong river delta, Land Use Policy, 73, pp. 20-28, (2018) [Google Scholar]
  17. Sentinel-2 Products Specification Document, pp. 35-36, (2021) [Google Scholar]
  18. Zh. Jiang, A.R. Huete, J. Chen, Y. Chen, J. Li, G.N Yan, X. Zhang, Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction, Remote Sensing of Environment, 101, 3, pp. 366-378, (2006) [Google Scholar]
  19. S. K. McFeeters, The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, 17:7, pp. 1425-1432, (1996) [Google Scholar]
  20. F. I. Diakogiannis, F. Waldner, P. Caccetta, C. Wu, ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data, ISPRS Journal of Photogrammetry and Remote Sensing, 162, pp. 94-114, (2020) [Google Scholar]
  21. A. H. Pickens, M. C. Hansen, M. Hancher, S. V. Stehman, A. Tyukavina, P. Potapov, B. Marroquin, Z. Sherani, Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series, Remote Sensing of Environment, 243, 117792, (2020) [Google Scholar]
  22. H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, Ian, S. Savarese, Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658-666. (2019) [Google Scholar]
  23. Geospatial Positioning Accuracy Standards Part 3: National Standard for Spatial Data Accuracy, pp. 3-10. (1998) [Google Scholar]
  24. Map Accuracy Standards, USGS Fact Sheet 171-99, (1999) [Google Scholar]

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