## 2 Algorithm Overview  
As shown in Figure 2, the algorithm is mainly composed of two parts: aircraft detection (at the start, highlighted in yellow), and aircraft tracking and activity recognition (highlighted in blue).  
A DCNN-based rotation detector, named R3 Det, was used for aircraft detection at airports. Considering that the high-value activities of aircraft at airports usually occur on the runway, we focused on detecting aircraft in the area around the end of the runway. For satellites operating in staring mode, the location of the airport runway area in the image can be easily determined by combining satellite orbit, satellite attitude, and ground geographic information. The smaller the detection image size, the faster the detection speed. We can effectively reduce the time-consuming nature of detection in the start frame by focusing on detecting aircraft in the area around the end of the runway.  
Due to the powerful rotation detector, the aircraft in the start frame are reliably detected and located with accurate envelopes. An efficient image matching method is then used to track the aircraft in subsequent frame images. The aircraft images detected in the start frame are used as the reference templates. The tracking procedure aims to find a part with the same size and the highest matching similarity as the template image within a certain search area in the current frame image. There are generally two approaches to solve this problem: grey value-based or feature-based matching. Compared with feature-based matching methods, such as edge-based matching [28], grey-based matching methods can maintain high robustness in the face of changing lighting and background conditions. Thus,  
Figure 2: Flow chart of the algorithm.  
the NCC-based [29] image matching method was used for tracking in this study. The corresponding search area image of each template was rotated within a certain angle range to generate multiple search area images. Matching of the template was then performed within these search area images. This can help find accurate matching results for some rotating aircraft targets, such as those that make turns.  
The tracking process obtains the position of each aircraft in the image, as well as its movement over time. Combined with the resolution of the satellite image and the acquisition of time information, we can easily obtain the speed (km/h) of the aircraft. Depending on the speed of each aircraft, its activity can be classified as parking, taxiing, or flying.  
Aircraft entry and exit detection is performed around the borders of the image. Aircraft entry detection is performed through background segmentation and under a size constraint. Aircraft exit detection is a comprehensive judgment based on the disappearance of the aircraft, as well as its position and speed when it disappeared.  
At the end of each loop, each template image and its corresponding search area are updated. Templates are periodically updated to cope with the influence of a changeable background. The next search area corresponding to each aircraft is instantly updated according to its current tracking result. This cycle continues, completing aircraft target tracking and activity recognition of subsequent images in turn.