论文标题
satimnet：结构化和统一的培训数据，用于增强卫星图像分类
SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification
论文作者
论文摘要
具有复杂建模（例如深神经网络）的自动监督分类需要代表性培训数据集的可用性。尽管存在可用于此目的的大量数据集，但它们通常是非常异构的且无法互操作的。在这种情况下，目前的工作具有双重目标：i）描述开源培训数据管理，集成和数据检索的过程，以及ii），以证明对遥感图像分类的不同源培训数据的实际使用。对于前者，我们提出了satimnet，这是一系列开放培训数据的集合，并根据特定规则进行结构和协调。对于后者，已经设计和配置了基于卷积神经网络的两种建模方法来处理卫星图像分类和分割。
Automatic supervised classification with complex modelling such as deep neural networks requires the availability of representative training data sets. While there exists a plethora of data sets that can be used for this purpose, they are usually very heterogeneous and not interoperable. In this context, the present work has a twofold objective: i) to describe procedures of open-source training data management, integration, and data retrieval, and ii) to demonstrate the practical use of varying source training data for remote sensing image classification. For the former, we propose SatImNet, a collection of open training data, structured and harmonized according to specific rules. For the latter, two modelling approaches based on convolutional neural networks have been designed and configured to deal with satellite image classification and segmentation.
