# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 1 Introduction  
Ship-detection algorithms from SAR images have been proposed by many researchers [1; 2; 3; 4; 5]. Most methods exploit the fact that ships on SAR images are much brighter than the sea surface due to the corner-reflection effect [6; 7]. That is, the ship is a target having a higher backscatter coefficient, and hence the ship is very bright in SAR imagery, while the sea surface is very dark in SAR imagery because it has a lower backscatter coefficient. Constant false-alarm rate (CFAR) has been widely used for ship detection. The CFAR detects ships by finding a threshold value using the probability density function (PDF) of background clutter [8; 9; 10; 11]. However, detection results from the CFAR algorithm can be biased by statistical variables such as mean and standard deviation, because the false-alarm rate must be fixed [12]. In addition, the CFAR algorithm has the disadvantages that it is very complex and time-consuming [13]. The adaptive threshold approach is very similar to CFAR in that it estimates the mean and standard deviation of an image using constant value c, but the approach has a drawback in that it does not consider the characteristics of background noise [14]. Recently, many studies have relied on image-based feature extraction (machine learning) methods, such as the artificial neural network (ANN) and support vector machine (SVM) methods [15; 16]. The machine-learning analyzes the input data by calculating weighting factors from the relation between input data and training set. Thus, results from the machine learning can be varied according to input data and training set [17]. This means that the performance of the machine-learning algorithm is largely dependent on the selection of input data and training sets. In order to choose the training set for ship detection, we can use automatic identification system (AIS) data if it is available, whereas we can select the training set from a map which is manually created if AIS data is not available [18]. Thus, the selection of training set used for ship detection is not very difficult. However, the selection of input layers used for ship detection is not easy. The limitations of ship detection using the machine-learning algorithm are as follows:  
Firstly, image characteristics have been ignored when the ANN approach is applied to the detection of a target. For a better detection of the detected target using machine learning, image characteristics of the detected target must be emphasized, while those of other targets should be not underlined. Consequently, the input layers of ANN must be created from the image after target-emphasis procedures are done. This means that we need to emphasize ships in an SAR image in order to detect ships from SAR images. However, in most previous studies, input layers have not been created from SAR images without ship emphasis processing [19; 20; 21; 22]. The SAR images have speckle noise, the ghost phenomena, the side-lobe effect, and so on. The SAR image characteristics affect the precision of ship detection. To remarkably reduce the negative effects in the ANN approach, we need to minimize these effects. Moreover, the Kompsat-5 SAR image has severe ghost phenomena and side-lobe effects. Additional steps to mitigate the effects should be applied to the Kompsat-5 image. Secondly, subset images have usually been used to validate the ship detection algorithm in the previous studies [17; 23]. When ships are detected from SAR images, the most important thing is to minimize the negative effects from background noise, the side-lobe effect, wave effect, land area effect and so on. Thus, it is a better choice that the algorithm performance is validated by using the full scene. Therefore, for a better performance of the ship detection using the ANN approach, the input layers of ANN should be created by minimizing the negative effects of the Kompsat-5 SAR image, and the detection performance should be tested and validated by using the full scene rather than the sub-image.