Automatic Flood Detection from Sentinel-1 Data Using a Nested UNet Model and a NASA Benchmark Dataset

IF 2.1 4区 地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Binayak Ghosh, Shagun Garg, Mahdi Motagh, Sandro Martinis
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引用次数: 0

Abstract

During flood events near real-time, synthetic aperture radar (SAR) satellite imagery has proven to be an efficient management tool for disaster management authorities. However, one of the challenges is accurate classification and segmentation of flooded water. A common method of SAR-based flood mapping is binary segmentation by thresholding, but this method is limited due to the effects of backscatter, geographical area, and surface characterstics. Recent advancements in deep learning algorithms for image segmentation have demonstrated excellent potential for improving flood detection. In this paper, we present a deep learning approach with a nested UNet architecture based on a backbone of EfficientNet-B7 by leveraging a publicly available Sentinel‑1 dataset provided jointly by NASA and the IEEE GRSS Committee. The performance of the nested UNet model was compared with several other UNet-based convolutional neural network architectures. The models were trained on flood events from Nebraska and North Alabama in the USA, Bangladesh, and Florence, Italy. Finally, the generalization capacity of the trained nested UNet model was compared to the other architectures by testing on Sentinel‑1 data from flood events of varied geographical regions such as Spain, India, and Vietnam. The impact of using different polarization band combinations of input data on the segmentation capabilities of the nested UNet and other models is also evaluated using Shapley scores. The results of these experiments show that the UNet model architectures perform comparably to the UNet++ with EfficientNet-B7 backbone for both the NASA dataset as well as the other test cases. Therefore, it can be inferred that these models can be trained on certain flood events provided in the dataset and used for flood detection in other geographical areas, thus proving the transferability of these models. However, the effect of polarization still varies across different test cases from around the world in terms of performance; the model trained with the combinations of individual bands, VV and VH, and polarization ratios gives the best results.

Abstract Image

利用嵌套 UNet 模型和 NASA 基准数据集从哨兵-1 数据中自动检测洪水
事实证明,在接近实时的洪水事件中,合成孔径雷达(SAR)卫星图像是灾害管理部门的有效管理工具。然而,其中一项挑战是对洪水进行准确的分类和分割。基于合成孔径雷达的洪水绘图的常用方法是通过阈值进行二元分割,但由于后向散射、地理区域和表面特征的影响,这种方法受到限制。最近,用于图像分割的深度学习算法取得了进步,在改进洪水检测方面展现出了巨大的潜力。在本文中,我们利用由美国国家航空航天局(NASA)和电气和电子工程师学会 GRSS 委员会联合提供的公开 Sentinel-1 数据集,提出了一种基于 EfficientNet-B7 骨干的嵌套 UNet 架构的深度学习方法。嵌套 UNet 模型的性能与其他几种基于 UNet 的卷积神经网络架构进行了比较。这些模型在美国内布拉斯加州和北阿拉巴马州、孟加拉国以及意大利佛罗伦萨的洪水事件中进行了训练。最后,通过对来自西班牙、印度和越南等不同地理区域洪水事件的哨兵-1 数据进行测试,比较了训练有素的嵌套 UNet 模型与其他架构的泛化能力。此外,还使用 Shapley 分数评估了输入数据的不同偏振波段组合对嵌套 UNet 和其他模型的分割能力的影响。这些实验结果表明,在 NASA 数据集和其他测试案例中,UNet 模型架构的性能与带有 EfficientNet-B7 主干网的 UNet++ 相当。因此,可以推断这些模型可以根据数据集中提供的某些洪水事件进行训练,并用于其他地理区域的洪水检测,从而证明了这些模型的可移植性。然而,在世界各地的不同测试案例中,极化的效果在性能上仍然存在差异;使用单个波段、VV 和 VH 以及极化比率组合训练的模型效果最佳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.20
自引率
2.40%
发文量
38
期刊介绍: PFG is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the interconnected field of geoinformation science. It places special editorial emphasis on the communication of new methodologies in data acquisition and new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general. The journal hence addresses both researchers and students of these disciplines at academic institutions and universities as well as the downstream users in both the private sector and public administration. Founded in 1926 under the former name Bildmessung und Luftbildwesen, PFG is worldwide the oldest journal on photogrammetry. It is the official journal of the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF).
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