## 4 Discussion
### Result Analysis  
The harbor target extraction results we obtained are shown in Figure 3. The test images we selected contain different backgrounds and a variety of harbor targets, and the results show that the proposed algorithm extracts harbor targets very well and successfully removes the pseudo target regions. Then, the quantitative analysis of the proposed method is based on the accuracy of the calculation. The formula for the evaluation is as follows:  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN}\times 100\% \tag{15}\]  
where \(TP\) denotes the number of true positive harbor targets, \(TN\) denotes the number of true negative harbor targets, \(FP\) denotes the number of false positive harbor targets, \(FN\) denotes the number of false negative harbor targets. Similarly, we use \(TPR\) and \(FPR\) to evaluate the true positive ratio and the false  
Figure 7: (**a**) Original image. (**b**) Extracting image luminance channel. (**c**) Processing results of LEPA algorithm. (**d**) Processing results of EC-SIFT algorithm. (**e**) Harbor candidate region extraction results. (**f**) Harbor target extraction results. positive ratio; for the harbor target extraction algorithm, we are pursuing high _TPR_ and low _FPR_. They are defined as follows:  
\[TPR=\frac{TP}{TP+FN}\times 100\% \tag{16}\]  
\[FPR=\frac{FP}{TP+FN}\times 100\% \tag{17}\]  
Other quantitative measurement methods are precision, recall, and F-measure, which are represented by \(P\), \(R\) and \(F_{m}\), respectively. Their definitions are given as follows:  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{18}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{19}\]  
\[F_{m}=\left(1+\beta^{2}\right)\times\frac{P\times R}{\beta^{2}\times P+R} \tag{20}\]  
If the extraction regions contain the complete harbor target, we use the intersection over union (_IOU_) to evaluate it:  
\[IOU=\frac{DR\cap GT}{DR\cap GT}\times 100\% \tag{21}\]  
where \(DR\) denotes Detection Result, \(GT\) denotes Ground Truth. We use 3000 images as test images--1600 images contain harbor and 1400 images do not contain harbor to get the extraction results for the experiment. Harbor targets are correctly extracted in 1527 existing target images (_TP_ = 1527), harbor targets are extracted incorrectly in 84 non-existent images (_FP_ = 84), no harbor targets are extracted in the 73 existing target images (_FN_ = 73), no harbor targets extracted in 1316 non-existent target images (_TN_ = 1316). Therefore, the accuracy of the calculation is 94.77%, the calculation result of _IOU_ is 88.43%, the results of other evaluation functions are shown in Table 3, so our algorithm proves to be effective. Table 3, Table 3, Table 3 and Figure 3 show a comparison of our algorithm with some other target extraction algorithms. We have chosen some typical harbor extraction algorithms, such as HDD [2 ], HDC [2 ], HDB [2 ] and HDM [2 ], and in the calculation of these methods, we used the same test dataset and training dataset as our method. The average processing times of these methods are shown in Table 3. All the experiments are carried out in the Visual Studio 2015 environment with 3.5 Ghz CPU, 16 GB RAM. It can be seen that our algorithm has higher extraction accuracy and lower computational complexity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Acc(\%)** & **TPR(\%)** & **FPR(\%)** & **IOU(\%)** \\ \hline HDD & 86.23 & 88.38 & 16.21 & 70.62 \\ HDC & 82.07 & 83.56 & 19.64 & 77.96 \\ HDB & 77.37 & 81.44 & 27.29 & 74.45 \\ HDM & 91.30 & 91.94 & 9.43 & 85.16 \\ Our method & 94.77 & 95.44 & 6.00 & 88.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy (Acc), true positive ratio (TPR), false positive ratio (FPR) and intersection over union (IOU) of our method and four competing methods.