An attention U-Net model for detection of fine-scale hydrologic streamlines

Xu, Zhiwu; Wang, Shaowen; Stanislawski, Lawrence V.; Jiang, Zhe; Jaroenchai, Nattapon; Sainju, Arpan Man; Shavers, Ethan; Usery, E. Lynn; Chen, Li; Li, Zhiyu; Su, Bin

doi:10.1016/j.envsoft.2021.104992

Cited by 42 publications

(24 citation statements)

References 37 publications

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“…This research expands the work of [33] and implements a U-net ANN architecture very similar to the model applied by [27], who also supply a graphic representation of the model. The U-net model is an FCN that avoids the use of dense connected layers, which reduces the number of tuning parameters and required computations compared to fully connected neural networks.…”

Section: U-net Model Architecturementioning

confidence: 90%

“…The loss function that is minimized by the model is the negative of the Dice's coefficient, which is a simple reproducible accuracy measure found useful for image segmentation [59]. Additional details and optimization of the U-net model for hydrography extraction are documented by [33].…”

Section: U-net Model Architecturementioning

confidence: 99%

“…Thus, the patch size has a large bearing on model accuracy and efficiency. Applying a U-net model on similar feature maps derived from 1 m lidar elevation and intensity data, [33] determined a 224 × 224 patch size is effective for extracting hydrographic features. An analytical window of the patch size (224 × 224 cells) is called a sample.…”

Section: Selection Of Training Samplesmentioning

confidence: 99%

“…Dice scores appear slightly more stable for models with 13 to 16 training watersheds, when training scores range from 0.89 to 0.90 and validation scores range from 0.71 to 0.72. While using a similar U-net model to predict hydrographic streams from lidar data, [33]…”

Section: Model Trainingmentioning

confidence: 99%

“…The work presented here capitalizes upon the IfSAR digital models by generating elevation-derived layers-such as, curvature, topographic position index, and geomorphons-that have been shown to reflect geomorphic conditions [12,[30][31][32]. Earlier work tested various iterations and parameters using object-based image analysis software to estimate the suitability of each layer for delineating waterbodies and streams [25,26,33]. The raster maps, or themes, found suitable are used as input data for U-net models.…”

Section: Introductionmentioning

confidence: 99%

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Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

et al. 2021

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Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area.

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Section: U-net Model Architecturementioning

confidence: 90%

Section: U-net Model Architecturementioning

confidence: 99%

Section: Selection Of Training Samplesmentioning

confidence: 99%

Section: Model Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

et al. 2021

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Aircraft segmentation in remote sensing images based on multi-scale residual U-Net with attention

Wang

Zhang

Huang

2023

Multimed Tools Appl

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Aircraft segmentation in remote sensing images (RSIs) is an important but challenging problem for both civil and military applications. U-Net and its variants are widely used in RSI detection, but they are not suitable for multi-scale aircraft segmentation in RSIs, due to the aircrafts in RSIs are relatively small with various orientations, different sizes, fuzzy illumination and shadow, obscure boundary and irregular background. To overcome this problem, a multi-scale residual U-Net with attention (MSRAU-Net) model is constructed for multi-scale aircraft segmentation in RSIs. A multi-scale convolutional module, two modified Respaths and two kinds of attention modules are designed and introduced into MSRAU-Net to extract the multi-scale feature and make the feature fusion between the contraction path and the expansion path more efficient. Different from U-Net, MSRAU-Net replaces the convolutional block of U-Net with the Inception residual block to help the U-Net architecture coordinate the features learned from aircrafts with different scales, and the residual module and attention module are introduced into the modified Respath to deepen the network layers and solve the gradient disappearing problem while extracting the more effective feature from RSIs. The experiments on the RSI dataset validate that MSRAU-Net outperforms the other networks, in particular for detecting the small aircrafts. Compared with attention U-Net and MultiMixUNet, the precision of MSRAU-Net is improved by 9.25 and 3.36, respectively.

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Image Segmentation of Rectal Tumor Based on Improved U-Net Model with Deep Learning

Zhou

Song

2021

J Sign Process Syst

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An attention U-Net model for detection of fine-scale hydrologic streamlines

Cited by 42 publications

References 37 publications

Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

Aircraft segmentation in remote sensing images based on multi-scale residual U-Net with attention

Image Segmentation of Rectal Tumor Based on Improved U-Net Model with Deep Learning

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