Few-shot remote sensing scene classification aims to identify unseen classes using only a small number of labeled samples. Considering the large intra-class variances and inter-class similarity of remote sensing scenes, most existing methods focus on feature extraction, ignoring the overfitting problem caused by insufficient samples. To this end, we propose a novel few-shot learning framework, called multibranch feature generation networks (MFGNets), which solves the few-shot scene classification from the source by online sample generation at the representation space. Specifically, we first build a feature generation net to transform the few-shot classification into a regular classification problem, in which the generated samples are achieved by combining the class-specific features with the sampled intra-class features. Then, to ensure the quality of the generated samples, we introduce two novel regularization terms: the intra-class diversity loss (ID-Loss) and the inter-class consistency loss (IC-Loss), which aid the model in generating more diverse samples. Furthermore, we introduce a scale-angle aware self-supervised pretext to learn scale-invariant and rotation-invariant features, improving the model’s feature representation capability in remote sensing scenes. We evaluate the proposed method on three publicly available datasets, namely UC_Merced, NWPU-RESISC45, and AID. Our approach has achieved state-of-the-art performance, with an improvement of more than 3.31%, 2.64%, and 6.86% on the most challenging 1-shot tasks, respectively.