UNet Combined With Attention Mechanism Method for Extracting Flood Submerged Range

Li, Wenmei; Wu, Jiaqi; Chen, Huaihuai; Wang, Yu; Jia, Yan; Gui, Guan

doi:10.1109/jstars.2022.3194375

Cited by 12 publications

(4 citation statements)

References 42 publications

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“…Flood forecasting constitutes a fundamental and paramount problem within the realm of smart hydrology, which has garnered significant attention from scholars over the past several decades [17]. In recent years, the integration of remote sensing data has emerged as a promising avenue to enhance the accuracy and reliability of flood forecasting models.…”

Section: A Machine Learning For Flood Forecastingmentioning

confidence: 99%

Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing

Jiang,

Chen,

Zhou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate and timely flood forecasting, facilitated by Remote Sensing technology, is crucial to mitigate the damage and loss of life caused by floods. However, despite years of research, accurate flood prediction still faces numerous challenges, including complex spatiotemporal features and varied flood patterns influenced by multivariable. Moreover, long-term flood forecasting is always tricky due to the constantly changing conditions of the surrounding environment. In this study, we propose a Heterogeneous Dynamic Temporal Graph Convolution Network (HD-TGCN) for flood forecasting. Specifically, we designed a Dynamic Temporal Graph Convolution Module (D-TGCM) to generate a dynamic adjacency matrix by incorporating a multihead self-attention mechanism, enabling our model to capture the dynamic spatiotemporal features of flood data by utilizing temporal graph convolution operations on the dynamic matrix. Furthermore, to reflect the impact of multiple meteorological and hydrological features on the heterogeneity of flood data, we propose a novel approach that utilizes multiple parallel D-TGCM for processing heterogeneous graph data and implements a fusion mechanism to capture varied flood patterns influenced by multivariable. Experiments conducted on a real dataset in Wuyuan County, Jiangxi Province, demonstrate that HD-TGCN outperforms the state-of-the-art flood prediction models in MAE, NSE, and RMSE, with improvements of 80.32%, 0.15%, and 73.99%, respectively, providing a more accurate flood forecasting method that will play a critical role in future flood disaster prevention and control.

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Section: A Machine Learning For Flood Forecastingmentioning

confidence: 99%

Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing

Jiang,

Chen,

Zhou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Then, it concatenates and/or adds those results and multiplies the resulting arrays to the original feature map to update original feature maps with global information. CBAM has been proven to improve CNN model performance in various tasks (Wang et al 2019, Li et al 2022b, Rajyalakshmi et al 2022.…”

Section: Explanation Of Sub-modules In Framework Ss and Samentioning

confidence: 99%

“…SAR imagery is popular for hydro-science applications where cloud pollution is common, for it is external illumination independent and can see through clouds (Yang et al 2020, Kong et al 2022, Li et al 2022a. Thanks to the advancement of data-driven methods, especially novel deep learning (DL) techniques, scientists can now extract useful information from SAR images with powerful data models in various tasks such as flood extent mapping (Aristizabal et al 2020, Bosch et al 2020, Li and Demir 2023, Li et al 2022b, wetland delineation (Salehi et al 2018), surface change monitoring and detection (Zhang et al 2020, Kseňak et al 2022), andobject (e.g., ship) detection (Yang et al 2020). The improved availability of SAR imagery will further facilitate advancements in those subdivisions and aspects.…”

Section: Introductionmentioning

confidence: 99%

Better Localized Predictions with Out-of-Scope Information and Explainable AI: One-Shot SAR Backscatter Nowcast Framework with Data from Neighboring Region

Li¹,

Demir²

2023

Preprint

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Synthetic Aperture Radar (SAR) provides 10-m weather-independent global Earth surface observations for various tasks such as land cover land use mapping, water body delineation, and vegetation change monitoring. However, the application of SAR imagery has been limited to retrospective tasks by a “first event then observation” rule. Recent studies have proven the feasibility of a one-shot forecast of backscatters of SAR imagery using meteorological driving forces, soil moisture, geomorphic factors, and previous SAR images collected for the target area. Although the approach is promising, spatial connectivity, more specifically, the influence of the status of surrounding areas on the target location has yet to be considered. To fill that gap, this study proposed two nowcasting frameworks that can integrate precipitation and soil moisture data collected from surrounding areas through spatial aggregation (SA) and by processing spatial series (SS), respectively. The catastrophic 2019 Central US Flooding was used as a case study with the goal of predicting backscatters of SAR imagery captured during the event. The results from SA, SS, and a framework that only considers localized input (S0) are compared against each other as well as with the benchmark performance created with persistence assumption. Results show that S0, SA, and SS outperform the benchmark. In addition, considering data from neighboring areas that contribute to the target location further improves prediction accuracy. Comparing the gradients of results considering/not considering additional data indicates that neighboring data can alter the model’s attention on each feature of the localized input matrix. The difference in gradients between SA and SS indicates the way the neighboring information is integrated also matters. The methodology proposed by this study can serve as a building block for more active usage of SAR imagery in forward-looking tasks such as early flood warning and response.

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“…Instead of using the CBAM in the U-Net throughout the skip connections like suggested by [48], we decide to use a similar approach to the one suggested by [49]. Specifically, we place these blocks at the encoder while down-sampling before each convolution layer, to ensure a more optimal feature extraction and make the network focus its attention on the main characteristics before losing information during down-sampling.…”

Section: Cbammentioning

confidence: 99%

An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction

Espinosa

McIntosh

Albu

2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

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Large amounts of underwater imagery are constantly collected for environmental monitoring studies, as they are essential for estimating marine biodiversity and abundance. However, this collected data has variable quality due to uncontrolled environmental factors that cause blur and color casting. We attempt to address this issue by proposing two novel methods for underwater image enhancement.The first part of the thesis presents a deep learning architecture that integrates elements from classical methods to simultaneously address blurriness and color casting on underwater imagery in real time. We use two parallel architectures trained in a generative adversarial network scheme (GAN) with channel and spatial attention blocks to retrieve color, and discrete wavelength transform to preserve high-frequency components. Our experiments show that our method outperforms the state-of-the-art related works with respect to the structured similarity index metric (SSIM). Qualitative comparisons with color-checkers also show notable improvements over related works.The second part of the thesis proposes an unsupervised deep-learning approach for underwater image enhancement, which eliminates the need for reference images for training. This is an important step forward as for real (not synthetic) underwater images there is no high-quality reference available. Our method is based on a mathematical model for image dehazing. We use three networks to estimate the transmission map, the atmospheric light, and the enhanced image and propose a new compound loss function. We achieve results comparable to state-of-the-art supervised methods with respect to the SSIM while performing optimally at near real-time inference speeds.

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UNet Combined With Attention Mechanism Method for Extracting Flood Submerged Range

Cited by 12 publications

References 42 publications

Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing

Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing

Better Localized Predictions with Out-of-Scope Information and Explainable AI: One-Shot SAR Backscatter Nowcast Framework with Data from Neighboring Region

An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction

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