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
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Zusammenfassung: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 network methods suffer from contextual information loss, resulting in a lack of ability to model long-distance dependencies on geographic features (such as ridges, rivers, drainage lines, and valleys) when handling DEM SR tasks. To tackle this issue, we propose a combining contextual information method by integrated attention mechanism (IAM) network for DEM SR (CIASR). It models both the dependence on long-range geographical features and local contextual information by using the attention mechanism. To achieve this goal, a residual module is designed to extract the low-level geographic features effectively. Then, we further propose an IAM to capture dependencies on geographic features. Specifically, a channel-spatial attention module and a self-attention module are adaptive to extract local and global contextual information, respectively. In addition, to take full advantage of the contextual information, we use a pixelshuffle module and a deformable convolution module to integrate geographic features. Compared with the convolutional neural network based DEM super resolution (SRCNN), super resolution ResNet network (SRResNet), feature-enhanced deep learning network (FEN), super-resolution via repeated refinement (SR3), Terrain feature-aware super resolution model (TfaSR), conditional encoder-decoder generative adversarial neural networks (CEDGANs), enhanced residual feature fusion network (ERFFN), and global-information-constrained deep learning network for DEM SR (GISR) methods, the experimental results show that the CIASR method has outstanding advantages in complex and uneven surface terrains.
Bibliographie:ObjectType-Article-1
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3423716