  Self-supervised learning (SSL) has gained widespread 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 images 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 (CNN) and vision transformers (ViT), 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 pre-trained using CMID achieve
better performance than other state-of-the-art SSL methods on multiple
downstream tasks. The code and pre-trained models will be made available at
https://github.com/NJU-LHRS/official-CMID to facilitate SSL research and speed
up the development of RS images DL applications.
