## 2 ROI Extraction
### Target Candidates Extraction  
After the detection of saliency maps, saliency regions are enhanced, and the background is suppressed. In order to locate positions of candidate targets in the image, binary segmentation is needed. Furthermore, these areas include ships and false alarms. We can eliminate some simple false alarms by characteristic parameters and obtain ROI candidates. In order to extract the characteristic parameters of saliency regions, such as length and width, it is necessary to obtain binary images of saliency maps. Therefore, an adaptive segmentation based on the Otsu method and GrabCut [27; 28] was executed to extract candidate target regions. The original GrabCut algorithm is an interactive segmentation model with high precision. In this algorithm, the Gaussian mixture model (GMM) is used to model the foreground and background region of the image through annotating artificially. The foreground and background areas of the model need to be defined in advance. The foreground should include the complete target as much as possible. Figure 5: The process of the quaternion construction, and the result of the proposed saliency model. (**a**) The original image covered by thick cloud. (**b**) The contrast map generated by Equation (11). (**c**) The Gabor transform map generated by Equation (12). (**d**) The blend feature map combining spatial correlation with texture feature generated by Equations (13)–(16). (**e**) The final saliency map based on Equation (20). The rest of the image will be the background. Any point in the image corresponds to a Gaussian component of the target or background. This obviously requires too many manual inputs in advance. Otsu is a classical threshold segmentation algorithm that is simple to calculate, but its accuracy is low in complex scenes. Therefore, we propose an adaptive segmentation method combining the two algorithms to obtain more accurate binary regions. Initial binary segmentation: Slices of ROIs are segmented to obtain binary images by the Otsu algorithm. One example (2 m resolution) is displayed in Figure 6 to show how the algorithm is executed. Figure 6b is the binary result of the Otsu method. We can see that the target region is not complete. External moment calculation: The external moment of the binary image is computed. The upper left point is (\(x_{upper},y_{upper}\)), and the lower right point is (\(x_{lower},y_{lower}\)). The region of the red border is the external moment. The upper left point (pink point) and lower right point (blue point) can be obtained, as shown in Figure 6c. As regards the definition of foreground and background regions, the external moment is extended 10 pixels to establish the foreground and background regions. The inside of the white solid line is the foreground, and the other part is the background, as shown in Figure 6d. In this way, the foreground and the background can be obtained by expanding the external moment without manual input. As regards GrabCut calculations, the GrabCut model iterates the energy model according to the input foreground and background region until the energy tends to be stable [27]. As regards the final binary segmentation, the final binary segmentation image is obtained by a GarbCut model. In the saliency map, the region above the threshold is defined as the ROIs, while the other is considered to be the background. The external moment is recalculated to facilitate the subsequent application. Figure 7 shows the ROI extraction of images in complex backgrounds with a 2 m resolution. We can obtain many ROIs with different sizes, including ship targets, broken cloud blocks, sea waves, and other false alarms, as shown on the second column in Figure 7. The third column is the segmentation results obtained by the adaptive segmentation. Some false alarms can be eliminated by relevant simple shape features including length, width, area, aspect ratio, and tightness. By applying simple shape analysis, large clouds, islands, and small waves can be ruled out.