 This paper investigates how to use deep convolutional neural networks, CNNs, for high-resolution remote sensing, HRS, scene classification. The authors propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully connected layers are regarded as the final image features. In the second scenario, they extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios can result in remarkable performance and improve the state of the art by a significant margin. The results reveal that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features. Moreover, they tentatively combine features extracted from different CNN models for better performance. This article was authored by Fanhu, Guesang Xia, Jingwen Hu, and others. We are article.tv, links in the description below.