## 3 DCNN-Based Aircraft Detection  
DCNN-based rotation detectors have achieved good detection results on existing remote sensing datasets, especially for aircraft object detection. In this study, a rotation detector named R3 Det [14] was used for aircraft detection in the start frame. Figure 3 shows the flowchart of aircraft detection based on the R3 Det detector. The ResNet network [30] was used as the R3 Det detector's backbone. Upon this, the feature pyramid network (FPN) and the feature refinement module (FRM) were constructed for rotation target prediction. The R3 Det detector was fine-tuned with the DOTA1.0 dataset upon the pre-trained ResNet152 model. When detecting aircraft, an input image of an arbitrary size was divided into manageable slices (1100 \(\times\) 1100 pixels) and each slice underwent the detecting process with our trained model [1]. Partitioning takes place via a sliding window with the slice's size and overlap (10% by default), as shown in Figure 3.  
We tested the aircraft detection detectors with 10 remote sensing images of different airports. The main parameters of the remote sensing images used for testing are shown in Table 1. These remote sensing images have the following characteristics. Figure 3: Flowchart of aircraft detection (R3 Det-based). * Includes multiple ground sampling distance (GSD) metrics from 0.2 m to 1.0 m.
* The images contain multi-type aircraft of different sizes. The orientation, distribution, and painting type of the aircraft are arbitrary. Military and civil aircraft are all included. * The image includes areas such as airport runways, apron, etc. * Imaging conditions include no clouds and thin clouds. The test results are shown in Table 1. TP (true positive) represents a correct detection result. FN (false negative) represents a missed detection result. FP (false positive) represents false detected results. Figure 4 shows the recall and precision for each test sample. Through analysis of the results, we concluded that:  
1. The smaller the ground sampling distance (GSD), the better the detection performance of aircraft targets. 2. Missed detections mainly occurred in the detection of aircraft parked next to each other on the tarmac. 3. False positives are helicopters wrongly detected as aircraft in low-resolution images. Figure 5 shows some of the detection results. All aircraft in these images were accurately detected with confidences larger than 0.8. Both military (red dotted ellipse) and civilian aircraft (blue dotted ellipse) were accurately detected. Aircraft were also accurately detected in remote sensing images affected by thin clouds and fog, as in Figure 5a,b. Aircraft \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Properties of Test Remote Sensing Images**} & \multicolumn{3}{c}{**Test Results**} \\ \hline
**Image** & \
\begin{tabular}{c} **Size** \\ **(Pixel)** \\ \end{tabular} &
\begin{tabular}{c} **GSD** \\ **(m)** \\ \end{tabular} & \multicolumn{1}{c}{**Number of Instances**} & \multicolumn{1}{c}{**TP**} & \multicolumn{1}{c}{**FN**} & \multicolumn{1}{c}{**FP**} \\ \hline IMG1 & 3203 \(\times\) 1370 & \textless{}0.2 & 28 & 28 & 0 & 0 \\ IMG2 & 4346 \(\times\) 1199 & \textless{}0.2 & 54 & 54 & 0 & 0 \\ IMG3 & 3328 \(\times\) 3072 & \textless{}0.3 & 24 & 24 & 0 & 0 \\ IMG4 & 3018 \(\times\) 1065 & \textless{}0.3 & 27 & 27 & 0 & 0 \\ IMG5 & 2413 \(\times\) 5118 & \textless{}0.3 & 30 & 30 & 0 & 0 \\ IMG6 & 3193 \(\times\) 5540 & \textless{}0.3 & 33 & 33 & 0 & 1 \\ IMG7 & 2318 \(\times\) 1914 & \textless{}0.3 & 101 & 100 & 1 & 0 \\ IMG8 & 4347 \(\times\) 1202 & \textless{}0.3 & 154 & 152 & 2 & 1 \\ IMG9 & 4096 \(\times\) 3584 & \textless{}0.5 & 54 & 54 & 0 & 0 \\ IMG10 & 7168 \(\times\) 4096 & \textless{}1.0 & 113 & 110 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties of each test remote sensing image and its test results. Figure 4: Precision and recall results of each test remote sensing image.