Remote sensing data has been widely used for various Earth Observation (EO) missions such as land use and cover classification, weather forecasting, agricultural management, and environmental monitoring. Most existing remote sensing data-based models are based on supervised learning that requires large and representative human-labelled data for model training, which is costly and time-consuming. Recently, self-supervised learning (SSL) enables the models to learn a representation from orders of magnitude more unlabelled data. This representation has been proven to boost the performance of downstream tasks and has potential for remote sensing applications. The success of SSL is heavily dependent on a pre-designed pretext task, which introduces an inductive bias into the model from a large amount of unlabelled data. Since remote sensing imagery has rich spectral information beyond the standard RGB colour space, the pretext tasks established in computer vision based on RGB images may not be straightforward to be extended to the multi/hyperspectral domain. To address this challenge, this work has designed a novel SSL framework that is capable of learning representation from both spectra-spatial information of unlabelled data. The framework contains two novel pretext tasks for object-based and pixel-based remote sensing data analysis methods, respectively. Through two typical downstream tasks evaluation (a multi-label land cover classification task on Sentienl-2 multispectral datasets and a ground soil parameter retrieval task on hyperspectral datasets), the results demonstrate that the representation obtained through the proposed SSL achieved a significant improvement in model performance.

翻译：遥感数据已广泛应用于各类地球观测（EO）任务，如土地利用与覆盖分类、天气预报、农业管理和环境监测。现有的大多数基于遥感数据的模型依赖于监督学习，需要大量具有代表性的人工标注数据进行模型训练，这既昂贵又耗时。近年来，自监督学习（SSL）使模型能够从数量级更大的未标记数据中学习表征。这种表征已被证明能提升下游任务的性能，并在遥感应用中具有潜力。SSL的成功高度依赖于预设计的预文本任务，该任务通过大量未标记数据向模型引入归纳偏置。由于遥感影像具有超出标准RGB色彩空间的丰富光谱信息，基于RGB图像的计算机视觉预文本任务可能难以直接扩展至多光谱/高光谱领域。为解决这一挑战，本研究设计了一种新颖的SSL框架，能够从未标记数据的光谱-空间信息中学习表征。该框架分别针对基于对象和基于像素的遥感数据分析方法设计了两种新型预文本任务。通过两个典型下游任务评估（在Sentinel-2多光谱数据集上的多标签土地覆盖分类任务，以及在高光谱数据集上的土壤参数反演任务），结果表明，通过所提出的SSL获得的表征显著提升了模型性能。