# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 4 Results and Discussion  
The X-band Kompsat-5 SLC image listed in Figure 1 was used for the validation of the proposed method. Since the Kompsat-5 image has severe ghost phenomena, the azimuth band-pass filtering was applied first. The fraction factor \(n\) = 0.5 was used for the filter, and hence the azimuth processing bandwidth of about 3100 Hz was reduced to that of about 2419 Hz. The ghost phenomena were effectively mitigated in the SAR image, while the spatial resolution in the azimuth direction was degraded from about 2.29 to 2.94 m. In order to reduce the speckle noise of the image, the multi-look operation of 5 \(\times\) 5 looks in the range and azimuth directions was applied to the image by considering the ratio of azimuth and ground-range pixel spacing. The sigma-naught image was created by considering the incidence angle, and converted into the decibel unit. To generate the intensity-differential image from the sigma-naught image, the parameters of the NL-means filter was estimated from the standard deviation of some land areas where the intensity of noise components is relatively high. The kernel size of 5 \(\times\) 5 and the filtering parameter of 5.0 were used for the NL-means filter. The NL-filtered image enhanced the edge of ship objects and reduced the image noise and the side-lobe effects, as shown in Figure 3b, Figure 3f or Figure 3j. The median filter was used to remove ships from the NL-means-filtered image. The kernel size of the median filter of 21 \(\times\) 21 in both the azimuth and range directions was used for this study, because the maximum length of ships was about 20 pixels. As shown in the Figure 3c, all of the ships disappeared in the median-filtered image, while the fish farms and offshore bars were smoothed but still preserved in the median-filtered image in Figure 3g or Figure 3k. Consequently, the pixel values of the ships in the intensity-differential image were much higher than those of the sea (Figure 3d). Moreover, the side-lobe effect was remarkably reduced, as seen in Figure 3d, and most of the land area, fish farms and offshore bars were removed in Figure 3h or Figure 3l. The intensity-differential image can raise the possibility of ship detection. One of the input layers, the texture image, was calculated by summing the squares of the difference between the sigma-naught image and the NL-means-filtered image. In order to enhance the ship objects in the texture image, the kernel size of 5 \(\times\) 5 and the filtering parameter of 3.0 were used for the NL-means filter. After the NL-means filter was applied to the sigma-naught image, the difference map was generated by subtracting the NL-means-filtered image from the sigma-naught image. Figure 4a, Figure 4e or Figure 4i show the sub-images of the difference image in the boxes A, C and D of Figure 1. In the figures, the object edges were enhanced, and most bright pixels came from ships or buildings. Since most ships and buildings are small, their pixel values could be preserved because most of their pixels can be considered as edges. Moreover, the object edges were enhanced in the texture image by the sum-of-square calculation using the window kernel of 5 \(\times\) 5, as shown in Figure 4b, Figure 4f or Figure 4j. As seen in Figure 4b, all of the ship pixels in the texture image were much brighter than other objects. This means that the ship objects in Figure 4b were emphasized through the sum-of-square calculation. The median filter, having the kernel size of 21 \(\times\) 21, was used for the removal of the ships in the texture image. The ships effectively disappeared in Figure 4c. Thus, the ships were much enhanced in the texture-differential image that was created by the difference between the texture image and median-filtered image (Figure 4d), while the pixel values in the fish farms and offshore bars were remarkably reduced (Figure 4d, Figure 4h or Figure 4l). This image can increase the ship-detection rate as well as reduce the false-detection rate.