Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, huge amount of training data is required to obtain promising results. In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. We evaluated our dataset with two widely used object detection methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications. Moreover, proposed architectures and results of this study could be used for transfer learning of different datasets and models for airplane detection.

翻译：从卫星图像中检测飞机是一项具有挑战性的任务，原因是图像背景复杂，且传感器几何特性与大气效应导致的数据采集条件存在差异。深度学习方法为飞机自动检测提供了可靠且精准的解决方案，但获得理想结果需要大量训练数据。本研究利用谷歌地球（GE）图像，通过标注每架飞机在图像中的边界框，创建了一个名为高分辨率飞机（HRPlanes）的新型飞机检测数据集。HRPlanes包含来自全球多个不同机场的GE图像，以呈现不同卫星获取的多样化地貌、季节及卫星几何条件。我们采用两种广泛使用的目标检测方法——YOLOv4和Faster R-CNN——对数据集进行了评估。初步结果表明，该数据集可成为未来应用的重要数据源和基准数据集。此外，本研究所提出的架构与结果可用于不同飞机检测数据集与模型的迁移学习。