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The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Surface Water Mapping by Deep Learning

Isikdogan, Furkan, Bovik, Alan C., Passalacqua, Paola·2017·IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
DOI 10.1109/jstars.2017.2735443
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Abstract

Mapping of surface water is useful in a variety of remote sensing applications, such as estimating the availability of water, measuring its change in time, and predicting droughts and floods. Using the imagery acquired by currently active Landsat missions, a surface water map can be generated from any selected region as often as every 8 days. Traditional Landsat water indices require carefully selected threshold values that vary depending on the region being imaged and on the atmospheric conditions. They also suffer from many false positives, arising mainly from snow and ice, and from terrain and cloud shadows being mistaken for water. Systems that produce high-quality water maps usually rely on ancillary data and complex rule-based expert systems to overcome these problems. Here, we instead adopt a data-driven, deep-learning-based approach to surface water mapping. We propose a fully convolutional neural network that is trained to segment water on Landsat imagery. Our proposed model, named Deep-WaterMap, learns the characteristics of water bodies from data drawn from across the globe. The trained model separates water from land, snow, ice, clouds, and shadows using only Landsat bands as input. Our code and trained models are publicly available at http://live.ece.utexas.edu/research/deepwatermap/.

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Crossref · 10.1109/jstars.2017.2735443

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9 of 9 references verified0 citation errors · 0 not checkable

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  • Terrain characteristics can influence the model's accuracy, as slope effects may lead to shadowed regions, influencing the pixel spectral response.

    “Additionally, terrain characteristics can influence the model's accuracy, as slope effects may lead to shadowed regions [57], influencing the pixel spectral response.”

    From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [57]

Citations

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Research works printed but not linked: 29

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Metadata

Authors
Isikdogan, F., Bovik, A. C., & Passalacqua, P.
Venue
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2017-11
Volume
10
Issue
11
Pages
4909-4918
ISSN
  • 1939-1404
  • 2151-1535
DOI
10.1109/jstars.2017.2735443
Cited by
292
References
9
Licence
Funders
National Science Foundation (CAREER/EAR1350336, FESD/EAR1135427, SEES/OCE-1600222)

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