遥感中的联合多模态自监督预训练：在甲烷源分类中的应用
Joint multi-modal Self-Supervised pre-training in Remote Sensing: Application to Methane Source Classification
AI总结：
针对遥感任务标注数据需求大的问题，本文探究利用多传感器遥感数据的多模态特性开展自监督预训练，评估了联合嵌入方法训练的编码器在甲烷源分类任务上的性能。
摘要：
当前深度学习方法被广泛用于解决计算机视觉和遥感特定任务，对标注数据的需求持续增长。但在很多情况下，根据开展可靠标注所需的专业知识不同，标注过程可能漫长且繁琐。为缓解对标注的需求，近期文献中提出了多种自监督方法，这类方法的核心原理是仅利用无标注数据样本学习图像编码器。在对地观测领域，可利用特定领域的遥感图像数据来改进这类方法。具体而言，借助每张图像关联的地理位置，能够交叉比对多个传感器采集的同一位置数据，得到同一地点的多视角观测。本文简要回顾了所谓联合嵌入方法的核心原理，探究了多种遥感模态在自监督预训练中的应用，并在甲烷源分类任务上评估了所得编码器的最终性能。
英文摘要：
With the current ubiquity of deep learning methods to solve computer vision and remote sensing specific tasks, the need for labelled data is growing constantly. However, in many cases, the annotation process can be long and tedious depending on the expertise needed to perform reliable annotations. In order to alleviate this need for annotations, several self-supervised methods have recently been proposed in the literature. The core principle behind these methods is to learn an image encoder using solely unlabelled data samples. In earth observation, there are opportunities to exploit domain-specific remote sensing image data in order to improve these methods. Specifically, by leveraging the geographical position associated with each image, it is possible to cross reference a location captured from multiple sensors, leading to multiple views of the same locations. In this paper, we briefly review the core principles behind so-called joint-embeddings methods and investigate the usage of multiple remote sensing modalities in self-supervised pre-training. We evaluate the final performance of the resulting encoders on the task of methane source classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
