# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 5 Conclusions  
In this study, deep learning was used to detect ships from X-band SAR images. Furthermore, to further improve the ship detection performance, a preprocessing method was proposed to reduce the noise in the SAR image and increase the contrast between the sea and the ship. In addition, to verify the performance of the preprocessing method proposed in this study, both the intensity image without preprocessing and the intensity image after conversion to the decibel image were used as training images, and the test results were compared. The best detection performance was obtained when the intensity image with a learning rate of 0.001 was used and when the decibel image and intensity difference/texture image combination with a learning rate of 0.00005 was used. The highest AP values of the intensity images, decibel images, and intensity difference/texture images were 89.78%, 92.34%, and 95.55%, respectively. When the training process was performed using intensity images, ship wake were more often falsely detected as ships than other training images, and the number of undetected ships was the highest. In the case of learning using the decibel images, it was found that the false detection and non-detection ratios of the ships were reduced by 3% and 1%, respectively, compared to the intensity images. When the training process was performed using the intensity difference and texture images that were generated through the preprocessing method proposed in this study, the false ship detection rate was similar to that using the decibel image, and the rate of the undetected ships decreased by 4% compared to the result using the intensity images. The level of noise and value distributions that are present in the image affects the non-detection rate and false positives in the ship detection process using deep learning. The intensity image has a very wide range of values and shows a Rayleigh distribution, which may have a negative impact on the deep learning process. In the case of the intensity difference and texture images generated through the preprocessing method proposed in this study, not only were the detected ships better characterized after learning from the two input images, but also the effects of non-detection and false detection could be reduced by minimizing the noise in the image. The results of this study suggest that when deep learning is performed using SAR images, better detection results can be achieved by reducing the noise of the SAR image and enhancing the contrast between the ship objects and the background. This preprocessing method may be useful and applicable not only for detecting ships from SAR images but also for detecting other objects. However, the proposed preprocessing method has a limitation: ships that are very close to the land can also be eliminated during the land masking to reduce the false detection rate of ships. Furthermore, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ship Pixel** & **Total Number of Ships** & **Intensity** & **Decibel** & **Intensity Difference + Texture** \\ \hline
10–19 & 11 & 3 & 1 & - \\
20–29 & 35 & 6 & 4 & 3 \\
30–39 & 60 & 5 & 6 & 4 \\
40–49 & 92 & 7 & 4 & 1 \\
50–59 & 58 & - & - & - \\
60–69 & 34 & 1 & 3 & 1 \\
70–79 & 12 & - & - & - \\
80–89 & 3 & - & - & - \\
90–99 & 4 & - & 1 & - \\
100–109 & 3 & - & - & - \\
110–119 & 1 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of undetected cases according to ship size. The size of the ship is the number of pixels of the longer side between the width and length of the ship. The number in the preprocessing column indicates the number of undetected ships in each size. because a large median filter with a size of \(81\times 81\) was used to enhance the contrast between the ship and the sea, the filtering process takes a very long time. Therefore, to overcome this limitation, it is necessary to improve the preprocessing method so that ships can be detected well, regardless of the adjacent presence of a land area in future studies.