Deep learning is a promising method for image classification, including satellite images acquired by various sensors. However, the synergistic use of geospatial data for water body extraction from Sentinel-1 data using deep learning and the applicability of existing deep learning models have not been thoroughly tested for operational flood monitoring. Here, we present a novel water body extraction model based on a deep neural network that exploits Sentinel-1 data and flood-related geospatial datasets. For the model, the U-Net was customised and optimised to utilise Sentinel-1 data and other flood-related geospatial data, including digital elevation model (DEM), Slope, Aspect, Profile Curvature (PC), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Buffer for the Southeast Asia region. Testing and validation of the water body extraction model was applied to three Sentinel-1 images for Vietnam, Myanmar, and Bangladesh. By segmenting 384 Sentinel-1 images, model performance and segmentation accuracy for all of the 128 cases that the combination of stacked layers had determined were evaluated following the types of combined input layers. Of the 128 cases, 31 cases showed improvement in Overall Accuracy (OA), and 19 cases showed improvement in both averaged intersection over union (IOU) and F1 score for the three Sentinel-1 images segmented for water body extraction. The averaged OA, IOU, and F1 scores of the ‘Sentinel-1 VV’ band are 95.77, 80.35, and 88.85, respectively, whereas those of ‘band combination VV, Slope, PC, and TRI’ are 96.73, 85.42, and 92.08, showing improvement by exploiting geospatial data. Such improvement was further verified with water body extraction results for the Chindwin river basin, and quantitative analysis of ‘band combination VV, Slope, PC, and TRI’ showed an improvement of the F1 score by 7.68 percent compared to the segmentation output of the ‘Sentinel-1 VV’ band. Through this research, it was demonstrated that the accuracy of deep learning-based water body extraction from Sentinel-1 images can be improved up to 7.68 percent by employing geospatial data. To the best of our knowledge, this is the first work of research that demonstrates the synergistic use of geospatial data in deep learning-based water body extraction over wide areas. It is anticipated that the results of this research could be a valuable reference when deep neural networks are applied for satellite image segmentation for operational flood monitoring and when geospatial layers are employed to improve the accuracy of deep learning-based image segmentation.

Satellite-based flood monitoring for providing visual information on the targeted areas is crucial in responding to and recovering from river floods. However, such monitoring for practical purposes has been constrained mainly by obtaining and analyzing satellite data, and linking and optimizing the required processes. For these purposes, we present a deep learning-based flood area extraction model for a fully automated flood monitoring system, which is designed to continuously operate on a cloud-based computing platform for regularly extracting flooded area from Sentinel-1 data, and providing visual information on flood situations with better image segmentation accuracy. To develop the new flood area extraction model using deep learning, initial model tests were performed more than 500 times to determine optimal hyperparameters, water ratio, and best band combination. The results of this research showed that at ‘waterbody ratio 30%’, which yielded higher segmentation accuracies and lower loss, precision, overall accuracy, IOU, recall, and F1 score of ‘VV, aspect, topographic wetness index, and buffer input bands’ were 0.976, 0.956, 0.894, 0.964, and 0.970, respectively, and averaged inference time was 744.3941 s, which demonstrate improved image segmentation accuracy and reduced processing time. The operation and robustness of the fully automated flood monitoring system were demonstrated by automatically segmenting 12 Sentinel-1 images for the two major flood events in Republic of Korea during 2020 and 2022 in accordance with the hyperparameters, waterbody ratio, and band combinations determined through the intensive tests. Visual inspection of the outputs showed that misclassification of constructed facilities and mountain shadows were extremely reduced. It is anticipated that the fully automated flood monitoring system and the deep leaning-based waterbody extraction model presented in this research could be a valuable reference and benchmark for other countries trying to build a cloud-based flood monitoring system for rapid flood monitoring using deep learning.

In this letter, we present a new semi-empirical approach for optimized dark spot detection. The proposed method is a C-band model that identifies oil spill candidates by correlating surface wind vectors and incidence angles with SAR threshold backscatter coefficients. Specifically, our approach combines insights from wind-wave generation and radar imaging theories with the 193 global Sentinel-1 SAR oil spill data. Demonstrating a balance between accuracy and simplicity, which is crucial in dark spot detection, it achieves an F1 score of 0.8 and operates without requiring parametric adjustments or iterative processes. Its integration of wind speed data could also reduce false alarms in low-wind scenarios. These results indicate potential improvements in the efficiency of automated oil spill detection systems and their application in creating a balanced deep learning dataset.

Juyoung Song, Duk-Jin Kim, Doyoung Lee, Hwisong Kim, Hyokbeen Lee. Korean J. Remote Sens. 2024;40:1483-91. https://doi.org/10.7780/kjrs.2024.40.6.3.9

Bridge detection from Synthetic Aperture Radar (SAR) is of great significance for infrastructure management, disaster prevention, and navigation automation. Although high-resolution SAR is increasingly accessible, exploiting Sentinel-1 remains advantageous due to its global coverage and accessibility. However, low spatial resolution of 20 m in Sentinel-1 products poses challenges in detecting small and indistinct bridges. To enhance bridge detection in low-resolution Sentinel-1 imagery, we propose a novel architecture that incorporates CycleGAN as an edge detector. CycleGAN generates detailed boundaries using 5 m land use maps, with enhanced bridge saliency. Since bridges often connect distinct edges like riverbanks and roads, this edge information assists Poly-YOLOv8, our chosen detector, in accurately localizing bridges. Our approach then integrates edge information through feature fusion module and feature alignment loss. Accordingly, the proposed SAR-EdgeYOLO achieves a precision of 98.2%, recall of 91.7%, and mAP of 94.8% at IoU 0.5, marking improvements of 3.9, 3.8, and 3.1 percentage points respectively over the baseline. The results demonstrate that CycleGAN-aided edge extraction effectively addresses the limitations of low-resolution remote sensing data, improving bridge detection accuracy and multi-class localization. This research can further contribute to the advancement of time-series infrastructure monitoring with wide applicability and higher accuracy.

MIMO radar systems are recognized for their robustness in low-visibility and adverse weather conditions, yet they face challenges such as low spatial resolution and incomplete data capture, particularly in static regions. To address these limitations, this study introduces an innovative framework designed to enhance the quality of MIMO radar data by integrating super-resolution and inpainting techniques utilizing stereo camera data as ground truth. The proposed approach includes sensor fusion system that spatio-temporarily synchronize heterogeneous data, integrated inpainting and super-resolution model, and optimized masking strategy to restore missing static regions. Quantitative evaluations demonstrate that our model substantially outperforms existing baseline methods, achieving a Peak Signal-to-Noise Ratio (PSNR) of 13.7232 dB, an Structural Similarity Index (SSIM) of 0.7597 and a Learned Perceptual Image Patch Similarity (LPIPS) score of 0.1814, indicating a marked enhancement in both the detail and integrity of radar images. These advancements extend the utility of MIMO radar across various applications including autonomous driving, object detection, and human activity recognition, providing a foundation for advancing radar-based spatial sensing technologies.