# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 5 Conclusions  
We showed an efficient method to detect ship targets from the Kompsat-5 SAR image using the artificial neural network (ANN) approach. The method is composed of three main steps: (1) input-layer generation, (2) the ship-probability map generation using the ANN approach and (3) the ship-object identification. For the reduction of the side-lobe effect, ghost phenomena and speckle noises, we used the azimuth band-pass, NL-means filters and the median filter. The intensity- and texture-differential images were generated by image processing from the SAR image, and used as the input layers to enhance ship objects. The training set was derived by the statistical approach and applied to the ANN approach. The ship-probability map was generated and the ship objects were highlighted in the probability map. We have tried to estimate an optimal thresholding value from the probability map. For this, the histogram of the map was created. From the histogram, the point that the PDFs of ship and non-ship objects cross was determined as the optimal threshold value. The threshold value was 0.67.  
The performance validation of our proposed method was carried out. The total number of 78 ships were found in the SAR image through visual analysis and identified ships. Through the process, 82 objects were identified as ship objects, and 77 objects among them were real ships, while five objects were from the ghost phenomenon. Thus, in this case, the recall and precision were about 93.9% and 98.7%, respectively. The false-detection rate was 6.1%. This means that about 99% of all ships can be detected, and the detected ships are true ships about 94% of the time.  
It is very important to reduce the ghost and side-lobe effects and speckle noises when we find ship objects from the Kompsat-5 SAR imagery. Their reduction can enhance the ship objects in SAR imagery. The proposed method effectively reduced these noise effects, and consequently, the ship objects were well-enhanced as well as detectable. Therefore, the proposed method was successfully applied to ship detection from the Kompsat-5 SAR image. Moreover, the proposed method can be applied to other SAR satellite images such as COSMO-SkyMed and TerraSAR-X by adjusting the filtering parameters of the proposed method. It would be also expected that the proposed method can be applied not only to ANN but also to other deep learning techniques, such as convolutional neural networks (CNN).  
This research (NRF-2015R1A2A2A01005018) was supported by Mid-career Researcher Program through National Research Foundation of Korea (NRF) grant funded by the Ministry of Education, Science and Technology (MEST).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Threshold** & \(N_{g}\) & \(N_{t}\) & \(N_{r}\) & **Recall (\%)** & **Precision (\%)** & **False Detection Rate (\%)** \\ \hline
0.67 & 78 & 82 & 77 & 93.9 & 98.7 & 6.1 \\ \hline \end{tabular}
\end{table}
Table 2: Recall, precision and false detection rate from the detection result of the test Kompsat-5 image. \(N_{g}\): number of ground truth, \(N_{i}\): number of detected ships, \(N_{r}\): number of correctly detected ships  
**Author Contributions:** [PERSON] conceived and designed the experiments. [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] helped the data processing for the SAR imagery. [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.