E3S Web Conf.
Volume 310, 2021Annual International Scientific Conference “Spatial Data: Science, Research and Technology 2021”
|Number of page(s)||15|
|Published online||15 October 2021|
Improving the efficiency of using deep learning model to determine shoreline position in high-resolution satellite imagery
Moscow State University of Geodesy and Cartography, Moscow, Russia
* Corresponding author: Nguyen Thanh Doan, email address: firstname.lastname@example.org
Nowaday, expanding the application of deep learning technology is attracting attention of many researchers in the field of remote sensing. This paper presents methodology of using deep convolutional neural network model to determine the position of shoreline on Sentinel 2 satellite image. The methodology also provides techniques to reduce model retraining while ensuring the accuracy of the results. Methodological evaluation and analysis were conducted in the Mekong Delta region. The results from the study showed that interpolating the input images and calibrating the result thresholds improve accuracy and allow the trained deep learning model to externally test different images. The paper also evaluates the impact of the training dataset on the quality of the results obtained. Suggestions are also given for the number of files in the training dataset, as well as the information used for model training to solve the shoreline detection problem.
Key words: remote sensing / coastline / shoreline / deep learning / convolutional neural network / deep convolutional neural network / NDVI / NDWI.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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