# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 4 Results and Discussion
However, the texture-differential image was a little noisy due to the sum-of-square calculation. An image-processing step can be further applied for the noise reduction. In this study, we did not consider the additional noise reduction step because the noise pixel values were very small. Figure 3: Procedure for the generation of a differential image, one of the input layers of artificial neural network (ANN): (**a,e,i**) the intensity images, (**b,f,i**) the NL-means filtered images, (**c,g,k**) the median filtered images and (**d,h,l**) differential images in the boxes A, C and D (see Figure 1). The intensity- and texture-differential images were applied as the input layers to the ANN approach. The training set was obtained by the statistical-threshold approach of Equations (1) and (2). The threshold values of 15.16 and 2.53 for ship and non-ship training selections, respectively, were estimated from the intensity-differential image, and then 67 training ship samples were selected from the statistical-threshold approach. The ANN approach was applied to the ship detection using the two input layers and the training set. Figure 5 shows the ship-probability map estimated by using ANN. The pixels in the ship-probability map have values between 0 and 1. The pixel value of '0' means that the pixel is not a ship at all, while the pixel value of '1' denotes that the pixel is definitely a ship. The sea surface had values as low as 0.1, while the ship objects had values as high as 0.85. If accurate coastline maps or large-scale topographic maps are available, the bridges can be masked out. In Figure 6a,b, the ghost and side-lobe effects were as low as 0.3. As shown in Figure 6c,d, the probability value in the fish farms and offshore bars was lower than the ship objects. Figure 4: Procedure for the generation of a texture-differential image, which is another input layer used for ANN: (**a**,**e**,**i**) the difference between sigma-naught and NL-means-filtered images, (**b**,**f**,**j**) the texture images, (**c**,**g**,**k**) the median-filtered texture images and (**d**,**h**,**l**) the texture-differential images in the boxes A, C and D (see Figure 1). In order to estimate the ship-thresholding value from the ship-probability map, the histogram of the ship-probability map was created (Figure 7). From the histogram, the point that the PDFs of ship and non-ship objects cross was determined, as shown in Figure 7. The threshold value of 0.67 was applied by determining the ship objects from the probability map. We calculated the recall and precision to validate the performance of the proposed method. The recall (sensitivity) measures how many objects are detected, and the precision denotes how many objects are correct among the detected objects. The recall and precision have a value between 0 and 1. The recall and precision can be defined as given by:  
\[\left\{ \begin{array}{l}Recall=N_{r}/N_{t}\\ Precision=N_{r}/N_{g}\end{array} \right., \tag{3}\]  
where \(N_{g}\) is the number of ground truth, \(N_{t}\) is the number of ships detected by ANN and \(N_{r}\) is the number of correctly detected ships. Figure 5: Ship-probability map obtained by using the ANN approach. The boxes A to D are used for the detailed analysis. We found the total number of 78 ships from the SAR image through visual analysis and identified ships by using threshold values of 0.67. When the threshold value of 0.67 was used, 82 objects were identified as ship objects, and 77 objects among them were real ships (while five objects were from ghost phenomena). Thus, in this case, the recall and precision were about 93.9% and 98.7%, respectively. The false-detection rate was 6.1%. Table 2 summarizes the recall, precision and false detection rate from the detection result. The result means that about 98% of all ships can be detected, and the false detection rate is about 98%. Figure 6: (**a-d**) The ship-probability maps magnified from the A to D boxes in Figure 5.  
Figure 7: Ship-probability map obtained by using the ANN approach.