 This paper proposes a new approach for remote-sensing image scene classification called CNN CapsNet. It combines the advantages of convolutional neural networks, CNN, and capsule networks, CapsNet. First, a CNN is used as an initial feature map extractor. This CNN was pre-trained on the ImageNet dataset and is then used to extract features from the input remote-sensing image. Next, the extracted features are passed through a CapsNet to further enhance the classification accuracy. The CapsNet is able to capture the spatial information of the features in the image, while also preserving the hierarchical structure of the features. Finally, the output of the CapsNet is fed into a classifier to produce the final classification result. Experimental results show that the proposed CNN CapsNet outperforms other state-of-the-art methods on three publicly available remote-sensing image datasets. This article was authored by Wei Zhang, Ping Tang, and Li Junzhao. We are article.tv, links in the description below.