论文标题
多模式作物类型分类融合了多光谱卫星时间序列，农民轮作和当地作物分布
Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution
论文作者
论文摘要
准确，详细且及时的作物类型映射对于机构来说是一个非常有价值的信息，以便根据公民的需求制定更准确的政策。在过去的十年中，可用数据的数量急剧增加，无论是来自遥感（使用Copernicus-Sentinel-2数据）还是直接来自农民（多年来提供现场作物信息以及有关作物轮作的信息）。然而，大多数研究仅限于使用一种模态（遥感数据或作物旋转），并且切勿将地球观测数据与诸如农作物旋转之类的领域知识融合在一起。此外，当他们使用地球观察数据时，它们主要将其限制在过去几年的数据中。在这种情况下，我们建议通过使用层次深度学习算法来解决土地使用和农作物类型分类任务，以模拟农作物旋转，例如语言模型，诸如语音信号之类的卫星信号，并将作物分布作为其他上下文矢量。与具有显着性能的经典方法相比，我们获得了非常有前途的结果，在28级的设置（.948）中将准确性提高了5.1点，而在10级环境中（.887）将Micro-F1提高了9.6分（.887），仅使用一组专家选择的兴趣。我们最终提出了一种数据启发技术，以使该模型能够在本季节结束前对农作物进行分类，该技术在多模式的环境中效果很好。
Accurate, detailed, and timely crop type mapping is a very valuable information for the institutions in order to create more accurate policies according to the needs of the citizens. In the last decade, the amount of available data dramatically increased, whether it can come from Remote Sensing (using Copernicus Sentinel-2 data) or directly from the farmers (providing in-situ crop information throughout the years and information on crop rotation). Nevertheless, the majority of the studies are restricted to the use of one modality (Remote Sensing data or crop rotation) and never fuse the Earth Observation data with domain knowledge like crop rotations. Moreover, when they use Earth Observation data they are mainly restrained to one year of data, not taking into account the past years. In this context, we propose to tackle a land use and crop type classification task using three data types, by using a Hierarchical Deep Learning algorithm modeling the crop rotations like a language model, the satellite signals like a speech signal and using the crop distribution as additional context vector. We obtained very promising results compared to classical approaches with significant performances, increasing the Accuracy by 5.1 points in a 28-class setting (.948), and the micro-F1 by 9.6 points in a 10-class setting (.887) using only a set of crop of interests selected by an expert. We finally proposed a data-augmentation technique to allow the model to classify the crop before the end of the season, which works surprisingly well in a multimodal setting.
