ABSTRACT

Near-real time water segmentation with medium resolution satellite imagery plays a critical role in water management. Automated water segmentation of satellite imagery has traditionally been achieved using spectral indices. Spectral water segmentation is limited by environmental factors and requires human expertise to be applied effectively. In recent years, the use of convolutional neural networks (CNN’s) for water segmentation has been successful when used on high-resolution satellite imagery, but to a lesser extent for medium resolution imagery. Existing studies have been limited to geographically localized datasets and reported metrics have been benchmarked against a limited range of spectral indices. This study seeks to determine if a single CNN based on Red, Green, Blue (RGB) image classification can effectively segment water on a global scale and outperform traditional spectral methods. Additionally, this study evaluates the extent to which smaller datasets (of very complex pattern, e.g harbour megacities) can be used to improve globally applicable CNNs within a specific region. Multispectral imagery from the European Space Agency, Sentinel-2 satellite (10 m spatial resolution) was sourced. Test sites were selected in Florida, New York, and Shanghai to represent a globally diverse range of waterbody typologies. Region-specific spectral water segmentation algorithms were developed on each test site, to represent benchmarks of spectral index performance. DeepLabV3-ResNet101 was trained on 33,311 semantically labelled true-colour samples. The resulting model was retrained on three smaller subsets of the data, specific to New York, Shanghai and Florida. CNN predictions reached a maximum mean intersection over union result of 0.986 and F1-Score of 0.983. At the Shanghai test site, the CNN’s predictions outperformed the spectral benchmark, primarily due to the CNN’s ability to process contextual features at multiple scales. In all test cases, retraining the networks to localized subsets of the dataset improved the localized region’s segmentation predictions. The CNN’s presented are suitable for cloud-based deployment and could contribute to the wider use of satellite imagery for water management.

摘要

具有中分辨率卫星图像的近实时水分割在水管理中起着至关重要的作用。传统上，使用光谱指数已经实现了卫星图像的自动水分割。光谱水分割受到环境因素的限制，需要有效运用人类专业知识。近年来，在高分辨率卫星图像上使用卷积神经网络（CNN）进行水分割已获得成功，但在中等分辨率图像上使用的程度较小。现有研究仅限于地理上本地化的数据集，并且已针对有限范围的光谱指数对报告的指标进行了基准测试。本研究旨在确定单个CNN是否基于Red，Green，蓝色（RGB）图像分类可以有效地在全球范围内分割水，并且优于传统的光谱方法。此外，这项研究评估了较小的数据集（具有非常复杂的模式，例如港口大城市）可以用来改善特定区域内全球适用的CNN的程度。来自欧洲航天局的Sentinel-2卫星（空间分辨率为10 m）的多光谱图像已获得。在佛罗里达，纽约和上海选择了测试地点，以代表全球范围内的各种水体类型。在每个测试站点上开发了特定于区域的光谱水分割算法，以表示光谱指数性能的基准。DeepLabV3-ResNet101在33,311个语义标记的真彩色样本上进行了培训。在三个较小的数据子集上对生成的模型进行了重新训练，特定于纽约，上海和佛罗里达。CNN预测在0.986和F1-Score的联合结果上达到的最大平均交集为0.983。在上海测试站点，CNN的预测优于光谱基准，这主要是由于CNN能够在多个尺度上处理上下文特征的能力。在所有测试案例中，将网络重新训练为数据集的局部子集可以改善局部区域的分割预测。所提供的CNN适用于基于云的部署，并且可能有助于更广泛地使用卫星图像进行水管理。主要是因为CNN能够在多个范围内处理上下文特征。在所有测试案例中，将网络重新训练为数据集的局部子集可以改善局部区域的分割预测。所提供的CNN适用于基于云的部署，并且可能有助于更广泛地使用卫星图像进行水管理。主要是因为CNN能够在多个范围内处理上下文特征。在所有测试案例中，将网络重新训练为数据集的局部子集可以改善局部区域的分割预测。所提供的CNN适用于基于云的部署，并且可能有助于更广泛地使用卫星图像进行水管理。