NDLI: Water Body Extraction from High-resolution Remote Sensing images Based on Scaling EfficientNets

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Water Body Extraction from High-resolution Remote Sensing images Based on Scaling EfficientNets

Content Provider	Scilit
Author	Lu, Li Qi, Wen Bing, Wang Shichao, Fan Li, Lingling Qingjie, Liu
Copyright Year	2021
Description	Journal: Journal of Physics: Conference Series Effectively and accurately extracting water bodies from high-resolution remote sensing images is an important yet extremely challenging task due to ambiguities raised by spectral and texture similarities between water bodies and distractors such as shadows buildings, mountains and vegetations. Recent developed deep learning techniques have provided researchers powerful tools to defeat traditional hand-engineered features by learning powerful hierarchical representations, however, with a cost of very high model complexity and computational resource requirement. To conquer this issue, in this paper, we propose to build a deep learning model for water extraction based on the EfficientNet-B5. We evaluate possible variations of such design and compare them with several widely used approaches on GF-2 and Sentinel-2 satellites. Experimental results demonstrate that our model obtains much better performance than SVM and U-Net, which clearly shows the effectiveness and robustness. We hope this work will facilitate further research in this field and inspire researchers in the community to develop better models.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1894/1/012100/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1894/1/012100
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	1894
Language	English
Publisher	IOP Publishing
Publisher Date	2021-04-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Urban and Regional Planning Resolution Remote
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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