# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
## 3 Experiments and Results
### Evaluation
#### 3.2.2 Quantitative Evaluation  
Figure 4 is the results of ship detection and tracking in two ROIs. In ship detection, the larger the threshold \(th\) of the saliency map is, the smaller the recall is and the larger the precision is. The empirical value of the threshold is 3-5, and it is set to 4 in the experiment. In Figure 4a,b, the units of intensity image are digital numbers (DN). From Figure 4a-d, we can see that the ROI 1 is homogeneous, but a lot of false targets are existed after ship detection. The ROI 2 is an inhomogeneous and traffic-intensive region where there are a lot of ships. In Figure 4c-h, 'AIS' and 'GF-4' mean the locations of AIS data and the locations of GF-4 detection reports, respectively. There is absolute positional deviation between the GF-4 detections and the ground truth locations directly using RPCs and the errors are about 10-60 pixels in the sample and line. As shown in Figure 4c,d, we can also see that the points of ship sailing look like curved instead of straight. This is because there is a position deviation between GF-4 satellite sequential images. Therefore, it is necessary to correct the position error before ship tracking. The results of GNN data association in the first frame and matching point pairs after RANSAC processing are shown in Figure 4e-h. The distance threshold \(\varepsilon\) in GNN association is set to 200, and the number of iteration in RANSAC processing is set to 1000. From the comparison of the subfigures, we can see that the performance of RANSAC is good and it can remove the erroneous point pairs effectively. In ship tracking, the main parameters are set as: \(\sigma_{p}=0.002^{\circ}\), \(\sigma_{v}=0.01\) nm/h\({}^{2}\), \(P_{D}=0.95\), \(\lambda_{f}=10^{-11}\), \(\sigma_{a}=15\), \(c_{1}=0.1\). For track management, the 3-of-4 test is used to confirm tracks and tracks are terminated after 2 missed detections. Figure 4i,j shows the tracking results in geographical coordinates after RPCs correction, where 'Detection' and 'Track' mean the detection reports (measurements) and the tracking results of our algorithm, respectively. A red line represents a track, where 'o' and '+' represent the start and of end of a track, respectively. Table 2 reports the calculated metrics of detection and tracking in five frames in two ROIs. \(TP\), \(FP\) and \(FN\) are confirmed by manual identification and AIS information comparison analysis. There are total 151 ships (53 ships in ROI 1 and 98 ships in ROI 2) in two ROI, and 79 tracked ships (22 ships in ROI 1 and 59 ships in ROI 2) is verified by real-time AIS data. From Table 2, we can see that there are many false targets before ship tracking. The false alarm rate is high in region 1 mainly because some inferences such as ocean waves are similar to ships. It is satisfactory that the total recall in two ROIs is above 80%. The false negatives (missing targets) in ship detection are small-size ships whose lengths are 50-100 m by the analysis of unassociated AIS data. Through MHT tracking algorithm, false targets can be reduced effectively, and true positives can be estimated accurately. The total F-score after tracking are both above 90%. Moreover, the improved MHT which combines target amplitudecan improve measurement-to-track data association. The improved tracker reacts less sensitive to false positives than the MHT only using position and has higher precision. Figure 4: _Cont._  
Figure 5 is the amplitude information of tracked ships. In Figure 5a, the amplitudes of the same target in five frames are represented by the same color. It can be seen from that the ship's amplitude is stable and distinguished. This is also the reason we use amplitude information for improving tracking performance. Figure 5b is the mean amplitudes of tracked ships compared with ship sizes in AIS data. We can see that the larger the size of the ship, the greater the amplitude value, and easier to be detected.