## 4 Experimental Results and Analysis
### Tests in Different Parameters  
In the third experiment, the performances of the proposed method were tested under different testing parameters using the Zhanjiang data. Because the performance of the proposed method is mainly affected by the thresholds of PV and PRDV parameters, the proposed method was tested under different C and FAR values. To evaluate how the performance varies under different PV thresholds, FAR was set to a constant of 0.05, and C was set to 5, 7, and 9 dB. The detection result is shown in Figure 9, where the first column in Figure 9a1-a3 corresponds to the C set to 5 dB, the second column in Figure 9b1-b3 corresponds to 7 dB, and the third column in Figure 9c1-c3 corresponds to 9 dB. The water and land segmentation results are shown in Figure 9a1-c1. Figure 9a1-c1 shows that the ratio of water pixels increases as value C increases. The ROI extraction results are shown in Figure 9a2-c2. Comparing Figure 9b2 with Figure 9a2, we can see that more water pixels are detected as an ROI in Figure 9a2. The reason for this is that the number of water pixels wrongly segmented to land increases with the decrease of C. Comparing Figure 9b2 with Figure 9c2, we can see that more land pixels are detected as an ROI in Figure 9c2. The reason for this is that the number of land pixels wrongly segmented to water increases with the increase of C. The ROI recognition results are shown in Figure 9a3-c3. We can see that the detected result in Figure 9b3 has the smallest difference from the ground truth. The IoU indexes are listed in the second row of Table 3. The highest value is 75.38% when C is set to 7 dB, but the value is 58.91% when C equals 5 dB and 54.09% when C equals 9 dB. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Zhanjiang** & **Tianjin** & **Fujian** & **Lingshui** & **Berkeley** \\ \hline IoU(\%) & 75.38 & 50.34 & 66.73 & 41.18 & 77.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The IoU indexes of the proposed method under different coastal scenes. Figure 8: Detection results in different coastal scenes: (**a1–f1**) The Pauli pseudocolor image of Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America; (**a2–f2**) the ROI detection result, where the extracted water pixels are drawn in gray, the detected interference water pixels are drawn in white, and the extracted ROI is marked in red boxes; (**a3–f3**) the ROI recognition result, where the detected port is marked in a red box and the ground truth is marked in a green  
box; **(a4-f4)** the zooming recognition result near the target port; **(a1-a4)** Zhanjiang; **(b1-b4)** Fujian; **(c1-c4)** Berkeley; **(d1-d4)** Tianjin; **(e1-e4)** Lingshui; **(f1-f4)** Boao. To evaluate how the performance is varied under different PRDV thresholds, C was set to a constant of 7 dB and FAR was set to 0.02, 0.05, and 0.09. The detection result is shown in Figure 10, where the first column in Figure 10a1-a3 corresponds to a FAR set to 0.02, the second column in Figure 10b1-b3 corresponds to 0.05, and the third column in Figure 10c1-c3 corresponds to 0.09. The interference water segmentation results are shown in Figure 10a1-c1. Figure 10a1-c1 shows that the ratio of the interference  
Figure 9: Comparison of the proposed method under different PV thresholds in the Zhanjiang data: **(a1–a3)** C is set to 5 dB; **(b1–b3)** C is set to 7 dB; **(c1–c3)** C is set to 9 dB; **(a1–c1)** the water and land segmentation results; **(a2–c2)** the ROI extraction results; **(a3–c3)** the ROI recognition results. water pixels increases with the increase of FAR. The ROI extraction results are shown in Figure 10a2-c2. Comparing Figure 10c2 with Figure 10a2 and Figure 10b2, we can find that more interference water pixels are detected as an ROI in Figure 10c2. The reason for this is that the number of normal water pixels wrongly segmented to the interference water increases with the increase of FAR. The ROI recognition results are shown in Figure 10a3-c3.