# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 4 Experimental Results and Analysis
### Experimental Results
#### 4.2.3 Results of Ship Detection with Simulated SAR Image  
The method that was designed in this paper was centered on detecting ships in large-size SAR images, so we tested the robustness of the proposed method on large-size SAR images. We used simulated SAR images to evaluate the robustness of the model. The image data source was Sentinel-1, and its format was a simulated HISEA-1 SAR image format. During the simulation process, regular black bands are inevitably generated in the images because of deviations in geometric corrections, but these did not affect the actual detection results. Table 3 presents the image information. In the CFAR detection, we set the false alarm rate to 0.001, and used the sliding window to extract targets that may be ships from the images. The detection result of the CFAR algorithm was binarized, and we mapped it to the original image and stored it as a 256 \(\times\) 256 pixel image patch. Figure 8 shows examples of the output results. On this test image, the total of 673 image patches were output by the CFAR algorithm, which included image patches with real ship targets and some false alarms. From the results, it can be seen that the CFAR algorithm was able to detect most ship targets, but false alarms were prone to occur in areas with complex sea conditions, and some sea waves were commonly detected as ship targets. At the same time, because some small islands and reefs in the ocean are not connected to land, they are not segmented during pre-processing, and they can also be detected as ship targets. Secondly, there are some ship-like targets that do not fully conform to a ship's characteristics, but the CFAR detector cannot completely filter them. Additionally, there are also some very small ships that were not detected. Because  
Figure 8: Examples of image patches after CFAR detection. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location 1** & **Resolution** & **Width 1** & **Height 1** & **Polarization** \\ \hline
31\({}^{\circ}\)1’29.21\({}^{\prime\prime}\)N 122\({}^{\circ}\)41’36.44\({}^{\prime\prime}\)E & 15 m & 9042 & 12,930 & VV \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulated SAR image specific information. 1 Location refers to the longitude and latitude of the center point of the image. The width is the number of range pixels and the height is the number of azimuth pixels. of the various false alarm sources, it is necessary to use deep learning to further assess the initial positive detection results and improve the ship detection accuracy. A true positive (TP) indicates that the predicted result was a ship, and it actually is a ship. A true negative (TN) means that the predicted result was not a ship and that the actual result is not a ship. In our experiment, we did not add negative samples into the dataset, so the occurrence of TN in the experiment was zero. The false positive (FP) indicates that, while the predicted result was a ship, in fact there is no ship in the image. The false negative (FN) indicates that the model predicted that there was no ship in the image, but the image does contain a ship. We used false alarm (FA), missing alarm (MA), recall, and overall accuracy (OA) to evaluate the detection performance. The definitions of these indicators are as follows:  
\[FA=\frac{FP}{TP+FP}, \tag{15}\]  
\[MA=\frac{FN}{TP+FN}. \tag{16}\]  
Precision is the ratio of true positives to the identified image:  
\[precision=\frac{TP}{TP+FP}. \tag{17}\]  
Recall represents the ratio of the number of detected ship targets (TP) to all ship targets in the image. \[Recall=\frac{TP}{TP+FN}. \tag{18}\]  
The overall accuracy is the most intuitive performance measure and it is a ratio of correctly predicted observations to the total observations. \[OA=\frac{TP+TN}{TP+TN+FP+FN}. \tag{19}\]  
Figure 9 and Table 4 shows the detection results of our proposed method and only using YOLOv4-tiny. The results verified that our proposed method was correct and effective in terms of accuracy and efficiency.