 In this paper, we explore the potential of using pre-trained convolutional neural networks, CNNs, for high-resolution remote sensing, HRS, scene classification. We propose two scenarios for generating image features from these CNNs, one involving extracting activations from fully connected layers and another involving dense features from the last convolutional layer at multiple scales. Experiments show that both scenarios outperform traditional low and mid-level features, resulting in improved accuracy and performance. Furthermore, combining features from different CNN models further improves the performance. This study demonstrates the effectiveness of using pre-trained CNNs for HRS scene classification and provides a promising direction for future research. This article was authored by Fan Hu, Guisong Xia, Jingwen Hu, and others.