Restricted Label-Based Self-Supervised Learning Using SAR and Multispectral Imagery for Local Climate Zone Classification

Deep learning techniques have garnered significant attention in remote sensing scene classification. However, obtaining a large volume of labeled data for supervised learning (SL) remains challenging. Additionally, SL methods frequently struggle with limited generalization ability. To address these limitations, self-supervised multi-mode representation learning (SSMMRL) is introduced for local climate zone classification (LCZC). Unlike conventional supervised learning methods, SSMMRL utilizes a novel encoder architecture that exclusively processes augmented positive samples (PSs), eliminating the need for negative samples. An attention-guided fusion mechanism is integrated, using positive samples as a form of regularization. The novel encoder captures informative representations from the unannotated So2Sat-LCZ42 dataset, which are then leveraged to enhance performance in a challenging few-shot classification task with limited labeled samples. Co-registered Synthetic Aperture Radar (SAR) and Multispectral (MS) images are used for evaluation and training. This approach enables the model to exploit extensive unlabeled data, enhancing performance on downstream tasks. Experimental evaluations on the So2Sat-LCZ42 benchmark dataset show the efficacy of the SSMMRL method. Our method for LCZC outperforms state-of-the-art (SOTA) approaches.