04. Shoreline detection using remote sensing data with deep learning technology

Nam Nguyễn Văn, Đoàn Nguyễn Thanh, Tùng Nguyễn Thanh

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.

Full text article

Generated from XML file

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).

Authors

Nam Nguyễn Văn
nvnam.tdbdv@hunre.edu.vn (Primary Contact)
Đoàn Nguyễn Thanh
Tùng Nguyễn Thanh
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
##submission.license.notAvailable##

Article Details

Similar Articles

1 2 3 4 5 6 > >> 

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

01. Monitoring mining activities by using satellite imagery data and UAV images: a case study in Yen Bai province

Huệ Lê Minh, Hiên Vũ Thị Thanh, Thảo Đỗ Thị Phương
Abstract View : 39
Download :19

13. Development of water turbidity monitoring system using the Internet of Things (IoT) additional to remote sensing data

Chung Doãn Minh, Quang Huỳnh Xuân, Nguyên Mai Thị Hồng, Đạt Đinh Ngọc, Ngọc Phạm Văn Bạch, Quân...
Abstract View : 66
Download :11

06. Study on soil salinity by using Sentinel-2 imagery data: a case study in Dong Nai Province, Vietnam

Huy Chu Xuân, Ngọc Nguyễn Minh, Đạt Đinh Ngọc, Thủy Lê Thu, Hải Hoàng, Huy Bùi Quang, Phong Trần...
Abstract View : 61
Download :14