## 4 Discussion
### Analysis of Parameter Selection  
In order to select more accurate parameters, we evaluated the performance of the harbor target extraction algorithm under different parameters. First, we determine the values of the SCOPE and MD in the LPEA. We use different SCOPE values to evaluate the effect of SCOPE on the selected image, which is shown in Figure 17. We evaluate the effect of different SCOPE values on the image by calculating the standard deviation of the image and the entropy of the image. Then, in order to explain that the algorithm keeps the useful edge information as much as possible, we calculate the total \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **TP** & **FP** & **FN** & **TN** \\ \hline HDD & 1414 & 227 & 186 & 1173 \\ HDC & 1337 & 275 & 263 & 1125 \\ HDB & 1303 & 382 & 297 & 1018 \\ HDM & 1471 & 132 & 129 & 1268 \\ Our method & 1527 & 84 & 73 & 1316 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: True positive (TP), false positive (FP), existing target images (FN) and non-existing target images (TN) of our method and four competing methods. Figure 8: (**a**) Original image. (**b**) Harbor extraction result based on dock. (**c**) Harbor detection result based on coastline closure. (**d**) Harbor detection result based on breakwater. (**e**) Harbor detection result based on model matching. (**f**) Harbor target detection result based on our method. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Average Running Time (s)** \\ \hline HDD & 11.15 \\ HDC & 6.36 \\ HDB & 5.14 \\ HDM & 24.37 \\ Our method & 2.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average time (s) of four competing methods and our method. number of edge pixels of the image and the number of edge pixels in the harbor region. The specific method is as follows:  
\[\delta=\sqrt{\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}(f(i,j)-\mu)^{2}} \tag{22}\]  
The above formula is the standard deviation calculation of the image: \(M,N\) represents the size of the image, \(f(i,j)\) is the pixel value at the coordinate \((i,j)\), and \(\mu\) is the average value of the image. This formula reflects the degree of dispersion between the individual pixel value and the mean value of the image. In this experiment, we hope that the standard deviation of the image to be as small as possible, which means that there are fewer details in the image. We use two-dimensional entropy as another evaluation function, and the specific formula is as follows:  
\[P_{ij}=c(m,n)/D^{2} \tag{23}\]  
\[H=\sum_{m=0}^{255}P_{mn}logP_{mn} \tag{24}\]  
where \(m\) is the gray value of the pixel, \(n\) is the mean value of the neighborhood gray, \(c(m,n)\) is the frequency of the binary group(\(m,n\)), \(D\) is the scale of the image, and the default value of \(D\) is 9. In this experiment, we hope that the value of entropy will be as small as possible, which means that the details of the image are small. Figure 10: Selected image. Figure 9: (**a**) Receiver operating characteristic (ROC) curves of our method and four competing methods with different colors that represent different methods. (**b**) Precision–recall curves of our method and four competing methods with different colors that represent different methods. Then we count the total number of edge pixels in the image and the number of edge pixels in the harbor region, which are summarized in Table 7 to illustrate the reliability of the parameters that we selected. The number of pixels in the image is 383,292, and the number of pixels in the harbor region is 7480. Using the above data, we have drawn the graphs shown in Figure 7. According to the comparison of these evaluation data, we should make sure that the available edge pixels are enough, and the standard deviation and the entropy should be as small as possible; therefore, the value of SCOPE equal to 150 is selected.