## 4 Experimental Results and Discussion
### Overall Detection Performances  
Finally, we compared our overall detection method with three typical methods, and the evaluation criteria are defined as follows:  
\[Accuracy=\frac{\text{\emph{The number of ships detected}}}{\text{\emph{The number of all targets}}(\text{\emph{ships and non - ships}})} \tag{36}\]  
\[\text{\emph{Missed alarm rate}}(\text{MA})=\frac{\text{\emph{The number of ships judged as background}}}{\text{\emph{The number of all ship targets}}} \tag{37}\]  
Figure 13: Objective comparison of different saliency algorithms. \[\text{False {alarm rate}}(\text{FA})=\frac{\text{{The number of non$-$ships judged as ship targets}}}{\text{{The number of all targets}}(\text{non$-$ships and ships})\text{{detected}}} \tag{38}\]  
We selected 500 typical panchromatic optical remote sensing images with a size of \(8192\times 4096\) pixels under different imaging conditions from the satellites GF-2 and ZY-3 and with a 2 m resolution. (Some images can be downloaded from the website: www.cresda.com/CN/). In addition, other panchromatic satellite images from various sea surfaces were collected and used as the testing dataset from the publicly available Google Earth service with a resolution of 2 m. These images are very large in size and we have subdivided them into 120 sub-images with \(8192\times 4096\) pixels. A total of 620 images contain ship targets of different sizes and shapes. We tested our approach on three groups of different sea surfaces: calm sea with little interference, a complex sea surface influenced by clouds and waves, the cloud area percentage of which is about 20%, \(-\)30%, and worse imaging conditions, the cloud area percentage of which is more than 50% and as high as 85%. The images contained ships of different types and sizes. The proposed method was implemented in C++ with Intel (R) Core (TM) i7-4770K CPU at 3.40 GHz and 64.0 GB RAM. The objective evaluation indices of the detection results are listed in Table 1. Because these images are so large in size that they will be compressed in this paper, we provided some local parts of the original images, as shown in Figures 14 and 15. Red borders indicate the ship targets we detected, and the yellow border areas indicate ships we missed with our method. In some cases, we lost some targets, as shown in of Figure 15a,d. In Figure 15a, one ship is almost completely obscured by clouds, making it difficult to locate the target, and another one is lost because the contrast of the ship and the background is too low, making it difficult to distinguish by the human eye. In Figure 15d, more than five ships are connected and docked, making it different from the conventional training model in terms of the shape and area; therefore, the targets were not recognized. In Table 1, we can see that with the increasing amount of interference, the accuracy of our method decreases slightly from Group 1 to Group 3. As shown in Figures 14h and 15h, our method lost some ships (areas indicated by yellow dotted lines) submerged under clouds, but the average accuracy reached up to 92.8% for different sea surfaces, and the average false alarm rate was 7.2%. Considering the existence of ships with different sizes in an image, the multi-scale saliency map extraction was designed so that the method can accurately identify ships of different sizes simultaneously. Images used in the experiment had a 2 m resolution. For ships smaller than 20 m (10 pixels), detection accuracy decreased, and in this case, targets would be so small that their feature parameters were sometimes inaccurate, affecting detection results. At the same time, we compared our method with state-of-the-art methods proposed in [7; 13; 17]. Table 2 was obtained by averaging the evaluation indices under different sea conditions, showing the average performance of the four methods. Table 3 shows a comparison of four methods under different sea conditions.