Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

中文翻译：

---

从 Sentinel-2 卫星图像时间序列中提取多层网络

如今，现代地球观测系统不断产生大量数据。一个值得注意的例子是由欧洲航天局作为哥白尼计划的一部分开发的 Sentinel-2 地球观测任务，该任务以高空间分辨率（高达 10 m）提供来自整个星球的图像，并且具有前所未有的重访时间（每 5 天）在赤道）。在这种数据丰富的场景中，遥感界对执行信息提取的现代监督机器学习技术（例如深度学习）表现出越来越大的兴趣，但往往低估了该框架所暗示的对参考数据的需求。相反，很少有人关注网络分析技术的使用，它可以为无监督信息发现提供一套强大的工具，受制于定义合适的策略来构建图像数据的类网络表示。这项工作的目的是通过提出一种从此类数据构建多层网络的方法，为如何使用复杂的网络模型有利地表示卫星图像时间序列提供线索。这是探索在遥感领域利用该模型的可能性的第一项工作。还介绍了在实际案例场景中通过提供的网络进行社区检测的示例，用于绘制复杂的土地利用系统，以评估这种方法的潜力。通过提出一种从这些数据构建多层网络的方法。这是探索在遥感领域利用该模型的可能性的第一项工作。还介绍了在实际案例场景中通过提供的网络进行社区检测的示例，用于绘制复杂的土地利用系统，以评估这种方法的潜力。通过提出一种从这些数据构建多层网络的方法。这是探索在遥感领域利用该模型的可能性的第一项工作。还介绍了在实际案例场景中通过提供的网络进行社区检测的示例，用于绘制复杂的土地利用系统，以评估这种方法的潜力。