04. Shoreline detection using remote sensing data with deep learning technology
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Submitted
15 March 2022
Published
31 December 2020
Keywords:
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Viễn thám Remote sensing
Abstract
This paper presented method identifying shorelines using remote sensing images and deep learning method. Experiments were conducted at two areas in Vietnam using Sentinel-2 satellite imagery. Comparing deep learning with traditional methods, such as support vector machine (SVM) and NDWI index shows that deep learning is an effective method for semantic segmentation remote sensing data. Measures for improving the accuracy of deep learning model when determining water surface were also proposed.
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References
[1]. Hanqiu Xu (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing. Vol. 27 N. 14.
[2]. Bangira, Tsitsi & Alfieri, Silvia & Menenti, Massimo & Van Niekerk, Adriaan. (2019). Remote sensing Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water. Remote Sensing. 11. 10.3390/rs11111351.
[3]. Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166 - 177.
[4]. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234 - 241). Springer, Cham.
[5]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770 - 778).
[6]. Pandey, R. K., Vasan, A., & Ramakrishnan, A. G. (2018). Segmentation of liver lesions with reduced complexity deep models. arXiv preprint arXiv:1805.09233.
[7]. Чернухин, Ю. В., Топчий, А. П., & Грязин, Е. А. (1997). Экспериментальное исследование скорости обучения нейросетей методом обратного распространения ошибки. Известия Южного федерального университета. Технические науки, 6 (3).
[8]. Маркеев, В. Ю., & Арзамасцев, А. А. (2013). Коррекция коэффициентов наклона функций активации нейронов методом обратного распространения ошибки. In современные методы прикладной математики, теории управления и компьютерных технологий (пмтукт-2013) (pp. 151 - 153).
[9]. Широких, Б., & Беляев, М. (2018). Влияние предобработки и аугментации данных на качество сегментации гиперинтенсивности белого вещества методами глубокого обучения. In ИТиС 2018 (pp. 117 - 124).
[2]. Bangira, Tsitsi & Alfieri, Silvia & Menenti, Massimo & Van Niekerk, Adriaan. (2019). Remote sensing Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water. Remote Sensing. 11. 10.3390/rs11111351.
[3]. Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166 - 177.
[4]. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234 - 241). Springer, Cham.
[5]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770 - 778).
[6]. Pandey, R. K., Vasan, A., & Ramakrishnan, A. G. (2018). Segmentation of liver lesions with reduced complexity deep models. arXiv preprint arXiv:1805.09233.
[7]. Чернухин, Ю. В., Топчий, А. П., & Грязин, Е. А. (1997). Экспериментальное исследование скорости обучения нейросетей методом обратного распространения ошибки. Известия Южного федерального университета. Технические науки, 6 (3).
[8]. Маркеев, В. Ю., & Арзамасцев, А. А. (2013). Коррекция коэффициентов наклона функций активации нейронов методом обратного распространения ошибки. In современные методы прикладной математики, теории управления и компьютерных технологий (пмтукт-2013) (pp. 151 - 153).
[9]. Широких, Б., & Беляев, М. (2018). Влияние предобработки и аугментации данных на качество сегментации гиперинтенсивности белого вещества методами глубокого обучения. In ИТиС 2018 (pp. 117 - 124).
Authors
Nguyễn Văn, N., Nguyễn Thanh, Đoàn, & Nguyễn Thanh, T. (2020). 04. Shoreline detection using remote sensing data with deep learning technology. Science Journal of Natural Resources and Environment, (34), 30–37. Retrieved from https://tapchikhtnmt.hunre.edu.vn/index.php/tapchikhtnmt/article/view/291
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