LAUSR

Self-supervised learning (SSL) has gained wide-spread attention in the remote sensing (RS) and Earth observation (EO) communities owing to its ability to learn task-agnostic representations without human-annotated labels. Nevertheless, most existing RS SSL methods are limited to learning either global semantic separable or local spatial perceptible representations. We argue that this learning strategy is suboptimal in the realm of RS since the required representations for different RS downstream tasks are often varied and complex. In this study, we proposed a unified SSL framework that is better suited for RS image representation learning. The proposed SSL framework, contrastive mask image distillation (CMID), is capable of learning representations with both global semantic separability and local spatial perceptibility by combining contrastive learning (CL) with masked image modeling (MIM) in a self-distillation way. Furthermore, our CMID learning framework is architecture-agnostic, which is compatible with both convolutional neural networks (CNNs) and vision transformers (ViTs), allowing CMID to be easily adapted to a variety of deep learning (DL) applications for RS understanding. Comprehensive experiments have been carried out on four downstream tasks (i.e., scene classification, semantic segmentation, object detection, and change detection) and the results show that models pretrained using CMID achieve a better performance than other state-of-the-art SSL methods on multiple downstream tasks. The code and pretrained models will be made available at https://github.com/NJU-LHRS/official-CMIDhttps://github.com/NJU-LHRS/official-CMID to facilitate SSL research and speed up the development of RS images DL applications.