Remote sensing scene classification is widely concerned because of its wide applications. Recently, convolutional neural networks (CNNs) have made a significant breakthrough in remote sensing image scene classification. However, the accuracy of using only fully connected layer of CNNs as a classifier is not satisfied, especially for few-shot remote sensing images. In this paper, we propose a framework of Feature-Fusion based Kernel Collaborative Representation Classification (FF-KCRC) for few-shot remote sensing images, which can make full use of the synergy between samples and the similarity between different types of image features to improve the accuracy of scene classification for few-shot remote sensing images. Specifically, we first design an effective feature extraction strategy to obtain more discriminative image features from CNNs, in which transfer learning is used to transfer the weights of pre-trained CNNs to alleviate the few-shot training problem. In the next, we design the FF-KCRC framework to make full use of the synergy between different categories and fuse the classification of different features, where kernel trick is used to address the problem of the linearly indivisibility. Extensive experiments have been conducted on publicly available remote sensing image data sets and the results show that the proposed FF-KCRC achieves stateof-the-art results.