## 4 Experimental Results and Discussion  
### Subjective Visual Evaluation of Saliency Models  
In order to test the performance of saliency map extraction proposed in this paper for panchromatic optical remote sensing images, four typical algorithms were compared and analyzed, and they were ITTI, SR, PQFT, and one found in [12]. The ASD and MSRA10K datasets were not used in the evaluation of various algorithms, because these two datasets are mostly color images [30]. Images with a 2 m resolution from the satellites GF-2 and ZY-3 were used to construct 300 test sets under various sea conditions such as different shooting time, conditions, and sea surface false alarms. The slice size is \(200\times 200\) pixels. In addition, in order to evaluate the performance of various algorithms objectively, we extracted precise ground-truth images of contours in advance. Figure 12 shows the ROI extraction in a set of typical cases, including the calm sea surface, low contrast, obvious sea clutter, and a similar texture. It is not difficult to find through a large number of experiments that, for panchromatic images, the performance of the frequency domain method is better than the spatial domain method. The spatial domain method cannot suppress the clutter interference and is greatly affected by strong waves and clouds. The SR and PQFT show no significant differences in performance. For the second image in Figure 12c,d, the ship target is completely submerged into the background and cannot be distinguished. The performance of the method found in [12] is better than other frequency domain algorithms, but the detected target area is incomplete, and the target easily melts into the background, as shown in the third, fourth, and fifth images in Figure 12f. The method from [12] only uses gray information, so ROIs cannot be accurately detected. Compared with other algorithms, the method of the saliency map extraction proposed in this paper obtained a better effect, despite a low contrast or a complex background. The detection result was more complete and clearer. Moreover, the brightness value distribution of the target area was uniform, which also helps to detect completed targets in the binary process. This will prevent to some extent incomplete target slices caused by too much brightness or too much darkness. We selected the PR curve and the comprehensive evaluation index F measure to evaluate the accuracy of various algorithms. Ground-truth images (manual marking) and binary images of saliency maps were recorded as G and M. In the PR curve, P and R refer to the precision and recall of the methods, respectively. Their calculation formulas are as follows:  
\[\mathrm{Precision}\ = \frac{|M\cap G|}{|M|}=\frac{TP}{TP+FP} \tag{33}\] \[\mathrm{Recall}=\frac{|M\cap G|}{|G|}=\frac{TP}{TP+FN} \tag{34}\]Among them, the pixels belonging to G and M are simultaneously called TP. The pixels belonging to G and not to M are called FN. The pixels belonging to neither G nor M are called FP. Recall and precision cannot be discussed in isolation. The F metric is introduced to comprehensively evaluate the detection performance of the saliency model as follows:  
\[F_{\beta}=\frac{(1+\beta^{2})\text{Precision}\star\text{Recall}}{\beta^{2} \text{Precision}+\text{Recall}} \tag{35}\]  
Figure 12: Saliency map extraction of different methods. (**a**) Original images. (**b**) Results of the ITTI method. (**c**) Results of the spectral residual (SR) method. (**d**) Results of the PQFT method. (**e**) Results of a method found in [12]. (**f**) Results of our method. Among them, the value of \(\beta\) is different, and the requirements of recall and precision are different. Combined with actual engineering requirement, this paper pays the same attention to recall and precision, \(\beta=1\). After experimental analysis, we selected ITTI in the spatial domain and the method in [12] in the frequency domain for comparison with our method. The performance of the two algorithms was relatively good in their domains.