Fusing Deep Features by Kernel Collaborative Representation for Remote Sensing Scene Classification

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 a fully connected layer of CNNs as a classifier is not satisfied, especially for few-shot remote sensing images. In this article, we propose a feature-fusion-based kernel collaborative representation classification (FF-KCRC) framework 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 pretrained CNNs to alleviate the few-shot training problem. Then, 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 linear indivisibility. Extensive experiments have been conducted on publicly available remote sensing image datasets, and the results show that the proposed FF-KCRC achieves state-of-the-art results.