# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 3 Methods  
In this study, we proposed an efficient method to detect ships from Kompsat-5 SAR imagery through the ANN approach. A key of the proposed method is that the input layer of the ANN approach uses two ship-emphasized images through minimizing the negative effects of the SAR image characteristics. Figure 2 presents the detailed workflow of the proposed method. The proposed method is categorized into (1) input-layer generation from a SAR image via SAR image processing, (2) ship-probability map creation through the ANN approach and (3) ship identification from the probability map using threshold value. For more-optimal input-layer generation, we need to (1) enhance ships in the SAR image and (2) reduce the negative effects of the SAR image characteristics, such as the speckle noise, ghost phenomena, side-lobe effect, and so on. Especially, since the negative effects are very severe in the X-band Kompsat-5 image, the effects should be corrected. The input-layer generation procedure is as follows:  
1. azimuth low-pass filtering, which is designed to mitigate the ghost phenomena,
2. multi-look processing, which is normally used for speckle noise reduction,
3. sigma-naught conversion, which includes a unit conversion,
4. intensity differential image creation,
5. texture differential image creation. Figure 2: Detailed workflow of the proposed method in this study. SAR: synthetic aperture radar; SRTM DEM: shuttle radar topography mission digital elevation model; NL: non-local. The azimuth low-pass filtering is used to mitigate the ghost phenomena. This filter is designed to reduce the azimuth processing bandwidth. If the azimuth processing bandwidth is large, the spatial resolution is high while the ghost effect is severe. Otherwise, low spatial resolution and reduced ghost effect can be seen in the SAR image. For the filter, the SAR SLC image is Fourier transformed in the azimuth direction, and then multiplied with the Hanning window having the bandwidth of \(n\)-PRF, where \(n\) is a fraction factor and PRF is the pulse repetition frequency. The fraction factor \(n\) depends on the characteristics of SAR sensor. Finally, we obtain the ghost-mitigated SLC image after the inverse Fourier transform is applied. The multi-look processing is used for the speckle noise reduction. It is well-known that this step improves SAR image quality [28; 29]. The multi-looked SAR image is converted into the sigma-naught image, which has a decibel unit. In order to create intensity- and texture-differential images, the NL-means and median filters are used. The PDF of the sigma-naught image is a normal distribution, and hence the NL-means filter can be properly applied to the sigma-naught image. The NL-means filter smooths images using weighting factors that are estimated from a similarity between the center and adjacent pixels. For this, the filter uses two moving window kernels, unlike conventional filters. The smoothing factors of the filter can be determined from the standard deviation of noisy areas. Generally, conventional smoothing filters reduce the noise component of images while they smooth the edge of objects. However, the NL-means filter can reduce the noise without smoothing the object edges. Thus, when the NL-means filter is applied to the ship detection, the edge of ships can be preserved while the noise can be well-reduced. The median filter estimates the median value from the window kernel, and hence the filter does effectively remove outliers. The filter has been widely used for smoothing SAR images due to the speckle noise characteristics. The median filter is used to remove small bright objects, such as ship objects, from the SAR image. The details for the intensity- and texture-differential image generation will be followed in the subsections. After the two input layers are created, the land areas in the input layers are masked out by using the SRTM DEM. Then, they are used for the ANN approach, and finally, the ship-probability map is created.