## 1 Introduction  
Aircraft activity recognition is of great significance in airport capability assessments and military intelligence acquisition, among other applications. Compared with ground-based monitoring methods, remote-sensing-based aircraft activity recognition can obtain results with wider coverage and more comprehensive information. Video satellites are a new type of earth observation satellite; compared with traditional remote sensing satellites, they can continuously observe a certain area and obtain more dynamic information using a staring mode. They are more suitable for tasks with time-sensitive characteristics, such as the recognition of aircraft activities at airports. However, it is difficult to meet the time requirements of emergency information acquisition with the traditional remote sensing process of detection, downlink transmission, and then processing. As shown in Figure 1 (green), the traditional method has poor timeliness and brings a lot of pressure on satellite-to-ground data transmission. It can cost hours to acquire remote sensing information, and a huge amount of remote sensing data needs to be downloaded. In addition, due to the constraints of orbital dynamics, a single satellite cannot achieve global monitoring, or all-day monitoring, of a certain area. To improve the coverage and duration of observations, satellite constellations are a good solution. However, more satellites mean higher budgets. Micro-satellites, on the other hand, have the advantages of low cost, a short development cycle, and the ability to form a network. A remote sensing satellite constellation based on micro-satellites is a better way to construct a global and all-day earth observation system. Additionally, on-board processing is the most straightforward way to improve the timeliness of observation tasks. As shown in Figure 1 (blue), the remote sensing image was processed on-orbit before the results were downloaded. As a result, the transmission volume is smaller, and the timeliness is improved. In summary, based on the micro-satellite constellations and their on-board processing capabilities, we can build a highly time-efficient global and all-day earth observation system. Therefore, the on-board processing capability of a single video satellite becomes very important. For the recognition of aircraft activities at airports, the onboard processing of aircraft detection and tracking must be solved first. However, this is not easy, as there are two challenges: the accuracy and efficiency requirements of the algorithm. In satellite imagery, objects of interest are often small and possibly densely clustered. Typically, the image GSD of video micro-satellites is around one meter. This means that the common civil aircraft will only be approximately 50 \(\times\) 50 pixels large in extent. Thus, targets have limited details in remote sensing images. Additionally, the objects can have arbitrary orientations when viewed from overhead [1]. Considering the above aspects, the performance requirement of an aircraft detection algorithm is relatively high. The algorithm should have enough recall rate and precision, as well as good positioning accuracy. In recent years, deep learning models, especially deep convolutional neural networks (DCNNs), have become state-of-the-art for many practical vision problems [2; 3]. Many DCNN-based detectors have been proposed in the research field of object detection. DCNN-based detectors can be divided into two types: two-stage object detectors [4; 5; 6; 7] and single-stage object detectors [8; 9; 10; 11]. Two-stage object detectors have achieved promising results on common benchmarks; however, their training process is complex and computationally expensive. In contrast, single-stage object detectors are currently state-of-the-art with respect to the trade-off between speed and accuracy.