Few-shot remote sensing scene classification (RSSC) confronts challenges due to its dependence on extensive labeled datasets. Addressing this, we propose the diversity-infused network (DIN), an unsupervised paradigm for few-shot RSSC, utilizing unlabeled data in training and adapting to novel classes with limited labeled samples. Within an augmentation-based framework, DIN includes a random augmentation sampling (RAS) strategy for task diversity in the meta-training stage, and a channel-driven metric learning (CDML) module to decode complex channel interactions, enhancing information diversity. Additionally, DIN presents a multimutual information (Multi-MI) objective function to balance the architecture and reduce the unreliability and potential biases from pseudotraining. Experimental results demonstrate that DIN surpasses other unsupervised approaches by over 11% in one-shot and nearly 9% in five-shot settings on WHU-RS19, and closely approaches supervised methods, with less than 1% difference in both settings.