Combining Contextual Information by Integrated Attention Mechanism in Convolutional Neural Networks for Digital Elevation Model Super-Resolution

High-resolution digital elevation models (HR DEMs) can provide accurate geographic information, which is widely used in flood risk evaluation, hazard mapping, and hydrological modeling. The study of DEM super-resolution (SR) algorithms has relieved the need for HR DEMs. However, most CNN backbone ne...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 62; S. 1 - 16
Hauptverfasser:	Chen, Zhanlong, Han, Xiaoyi, Ma, Xiaochuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Attention Attention mechanism Attention mechanisms contextual information Convolution Convolutional neural networks Deep learning Deformation effects dependence of long-range geographical features digital elevation model super-resolution (DEM SR) Digital Elevation Models Elevation Encoders-Decoders Environmental risk Feature extraction Formability Information processing Machine learning Modules Neural networks Ridges Risk assessment Rivers Superresolution Task analysis
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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