1. Disaster detection from aerial imagery with convolutional neural network;Amit,2017
2. Local semantic enhanced convnet for aerial scene recognition;Bi;IEEE Transactions on Image Processing,2021
3. All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classification;Bi;IEEE Transactions on Geoscience and Remote Sensing,2022
4. Imaging using unmanned aerial vehicles for agriculture land use classification;Chen;Agriculture,2020
5. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251–1258).