Abstract. Scene classification plays an important role in remote sensing field. Traditional approaches use high-resolution remote sensing images as data source to extract powerful features. Although these kind of methods are common, the model performance is severely affected by the image quality of the dataset, and the single modal (source) of images tend to cause the mission of some scene semantic information, which eventually degrade the classification accuracy. Nowadays, multi-modal remote sensing data become easy to obtain since the development of remote sensing technology. How to carry out scene classification of cross-modal data has become an interesting topic in the field. To solve the above problems, this paper proposes using feature fusion for cross-modal scene classification of remote sensing image, i.e., aerial and ground street view images, expecting to use the advantages of aerial images and ground street view data to complement each other. Our cross- modal model is based on Siamese Network. Specifically, we first train the cross-modal model by pairing different sources of data with aerial image and ground data. Then, the trained model is used to extract the deep features of the aerial and ground image pair, and the features of the two perspectives are fused to train a SVM classifier for scene classification. Our approach has been demonstrated using two public benchmark datasets, AiRound and CV-BrCT. The preliminary results show that the proposed method achieves state-of-the-art performance compared with the traditional methods, indicating that the information from ground data can contribute to aerial image classification.
Residual Dense Network Based on Channel-Spatial Attention for the Scene Classification of a High-Resolution Remote Sensing Image