PDF(Pubmed)
Abstract：
Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea-land segmentation.
摘要：
随着时间的推移监测海岸线对于快速识别和缓解海岸侵蚀等环境问题至关重要。利用卫星图像进行监测有两大优势,即，全球覆盖范围和频繁的测量更新；但需要适当的方法从此类图像中提取海岸线信息。为此,有一些有价值的非监督方法，但是最近的研究集中在深度学习上，因为它在一般性方面具有更大的潜力，灵活性,和测量精度，which,相比之下,从标记样本的大型数据集中包含的信息中得出。首先要解决的问题,因此,在于获得适合此特定测量问题的大型数据集，这是一项艰巨的任务，通常需要人类对大量图像进行分析。在这篇文章中,我们提出了一种技术，自动创建适合训练机器学习模型的标记卫星图像数据集的海岸线检测。该方法基于卫星照片数据和认证数据的整合，可公开访问的海岸线数据。它涉及几个自动处理步骤，旨在建立尽可能好的数据集，图像包括海洋和陆地区域，和正确的标签也存在复杂的水边（可以是开放或封闭的曲线）。使用独立认证的测量来标记卫星图像，避免了通过视觉检查手动注释它们所需的大量工作，正如文献中的其他作品所做的那样。当考虑到复杂的海岸线时，尤其如此。此外,也消除了由于卫星图像的主观解释而导致的可能错误。该方法被开发并专门用于构建新的Sentinel-2图像数据集，表示为SNOWED；但适用于经过微小修改的不同卫星图像。SNOWED中标签的准确性直接取决于所使用的海岸线数据的不确定性，这在大多数情况下导致子像素误差。此外，SNOWED数据集的质量是通过对图像及其相应标签的随机样本进行视觉比较来评估的，通过训练用于海陆分割的神经模型来显示其功能。
