Extraction of Water Bodies from Satellite Imagery Using AI Techniques

運用AI技術進行衛星影像的水域辨識

林威昕(Daniel-Wilson Lin) ； 張揚暉(Colo Chang)

《航測及遙測學刊》 27卷4期 (2022/12) Pp. 237-246

https://doi.org/10.6574/JPRS.202212_27(4).0003

摘要

Water body segmentation from remote sensing imagery is essential for monitoring and protecting water resources, as well as for assessing the risks of disasters such as flooding. However, traditional index-based approaches to water body identification have significant limitations. In this study, we applied and trained a convolutional neural network (CNN) called U-Net on FORMOSAT-5 imagery of the greater Tainan City area to identify water bodies. The experimental results of the U-Net model were compared with the Normalized Difference Water Index (NDWI) and convincingly showed that the U-Net model had achieved significantly better water body detection performance.

關鍵字

FORMOSAT-5 ； NDWI ； U-Net ； Water Segmentation ； Convolutional Neural Network (CNN)

並列摘要

許多衛星影像的分析應用（例如：分析熱島效應、揚塵區域、河道變遷、水資源的監測與保護、評估洪水災害）都仰賴於正確地找出水域範圍，進而才能夠得到有意義的分析結果。目前，最普遍被用於衛星影像的水域偵測方法為常態差異化水體指標（Normalized Difference Water Index, NDWI），但我們觀察到該方法用於福衛五號衛星影像時，要找出水域會有一些問題（例如容易將道路、建築物誤判為水域）。基於這樣的觀察，我們率先嘗試運用深度學習技術來進行福衛五號衛星影像的水域分割。我們採用的卷積神經網路架構被稱為U-Net，實驗結果顯示相較於NDWI，U-Net的水域分割準確率有著顯著提升。

並列關鍵字

福爾摩沙衛星五號 ； NDWI ； U-Net ； 水域分割 ； 卷積神經網路

參考文獻

Ronneberger, O., Fischer, P., and Brox, T., 2015. U-Net: Convolutional networks for biomedical image segmentation, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, pp. 234–241.

An, S.H., and Rui, X.P., 2022. A high-precision water body extraction method based on improved lightweight u-net, Remote Sensing, 14(17): 4127.

CARTO, 2017. Introducing Voyager: The New CARTO Basemap, Available at: https://carto.com/blog/new-voyager-basemap/, Accessed October 08, 2022.

Feyisa, G.L., Meilby, H., Fensholt, R., and Proud, S.R., 2014. Automated water extraction index: A new technique for surface water mapping using landsat imagery, Remote Sensing of Environment, 140: 23–35.

Kingma, D.P., and Ba, J., 2014. Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980.