## 4 Experiments and Discussion  
### Results  
This section analyzes the performance of the proposed method (Figure 6). In order to test the reliability of the proposed approach, we have used the test dataset described in Table 1. This dataset represents a set of emergency missions whose purpose is to automatically detect oil slicks using an onboard sensor, such as a SLAR. The feature of the images was described in Section 2.  
We applied our approach to several images, always using the same configuration of parameters described in Section 3.1. These values were optimized by tuning the parameters during the experiments until the best visual detection results were achieved. Other parameters are automatically computed in an adaptive manner, signifying that they do not have to be tuned. One example of this is the automatic estimation of seeds from the saliency map, as described in Sections 3.2 and 3.3. Unlike others, such as the works shown in [9; 10; 11; 12; 27; 28; 29; 30], the proposed approach always uses full images of the mission as input without using pre-processing masks to eliminate both land and noise pixels. The processing of the full image complicates the detection process but helps achieve a high level of automation. Keeping these issues in mind, we show the capability of our approach as regards detecting different types of oil slicks (i.e., considering the different size, shape, thickness and density of the spill, etc.) in several flight conditions (i.e., varying the wind speed on the sea surface between [0, 27] kn. and with altitudes of between [1061, 4596] ft. In general, all the images were acquired at dusk or at night (Figure 6a) and the results obtained were analyzed by comparing them with a ground truth (Figure 6b). The ground-truth includes all the pixels belonging to the region considered as a true oil slick. The labels of ground-truthpixels are provided to identify our target. Labeled oil slicks are necessary to quantitatively measure the performance when comparing different approaches for segmentation processes.  
The ground-truth images are segmented images with human-annotation. The ground-truth was manually done by authors according to help supplied by a flight operator and a marine biologist. To carry out oil-slick annotation, we marked the boundary, pixel by pixel. Later, we filled the area  
Figure 6: Results of oil-slick detection process with reconstruction: (**a**) Original SLAR image; (**b**) Ground-truth for oil slick area; (**c**) Our oil-slick detection guided by saliency maps and centroids.  
within the boundary using as criteria the visual perception of the human by comparison among the gray value of each interior pixel with both values, mean of boundary and neighboring pixels.  
Figure 6c illustrates the results obtained using our approach. As can be seen, a few pixels are considered to be FP. Moreover, the detection method is successful if it is necessary to estimate both the size and shape of oil slicks.  
In order to evaluate the performance of the proposed method, we have used the Jaccard index which is a metric widely used for this kind of tasks. It also is known as Intersection over Union (IoU). The Jaccard index \(J\) is a similarity measure between the segmented image \(I_{r}(x,y)\) (Figure 6a) and the ground-truth \(I_{gt}(x,y)\) (Figure 6b). This way, we compare the performance of our approach to correctly detect oil slicks. The Jaccard index is computed as:  
\[J\left(I_{r}(x,y),I_{gt}(x,y)\right)=\frac{I_{r}(x,y)\cap I_{gt}(x,y)}{I_{r}(x,y)UI_{gt}(x,y)}\approx\frac{TP}{TP+FP+FN} \tag{7}\]  
The results obtained using Jaccard index are 0.99, 0.87 and 0.76, for the three examples shown in Figure 6. Therefore, the number of pixels detected as TP (number of correctly detected pixels) is much bigger than the sum of FP (number of incorrectly detected pixels) and FN (non-detected or missed pixels) according to the mathematical estimation described in Equation (7).