## 3 Method
### Reconstruction of Oil Spills  
The detection of candidate oil-spill regions is not sufficient when the purpose is to analyze the changes in the shape and size of the oil slick over time. The SLAR sensors are employed on emergency missions to map, monitor and analyze sea surfaces from aircraft in order to seek oil spills, detect the ships that have caused them and evaluate the disaster according to track the boundary of the oil spill. A reconstruction method must, therefore, be applied to the SLAR image after detecting the candidate regions. The basis of the reconstruction method is a segmentation process that is based on region growing and is used to reconstruct the contour of the oil slick from pixels of candidate regions obtained from the saliency map. The reconstruction, therefore, makes it possible to define the contour pixels  
Figure 5: Example of candidate region detection process: (**a**) Original SLAR image; (**b**) New image without noise (i.e., we have applied the process shown in Figure 4); (**c**) Saliency map with spectral residue; (**d**) Thresholding and centroids.  
of the candidate region and to determine both its size (i.e., using perimeter and area) and shape (i.e., shape descriptor). A comparison of its shape and size at different times is then used to estimate how the oil dissolves in the water, which depends on the oil density, the tides and winds, etc.  
The region growing segmentation requires the selection of initial seed pixels to implement the process of pixels clustering. The grayscale value of a seed pixel is used as a reference value to determine the mean homogeneity that the neighboring pixels should have for them to be added to a same cluster. In our work, we use the centroid coordinates of candidate regions obtained from saliency map as seed pixels that will be applied to the original image (SLAR image without any processing). There will consequently be as many seed pixels as the candidate regions that were detected. The centroid is computed by using moments of order up to 1 [39].  
In order to perform the process of adding pixels to a cluster, we use an 8-connected neighborhood. Hence, if any adjacent pixel that considers 8-connectivity has a grayscale value similar to the seed pixel, then it is added to the cluster of the seed pixel, and they are classified as new seed pixels for the next iteration of the algorithm. The region growing algorithm follows an iterative process until there are no changes in two successive iterative stages, i.e., the new pixels are not added to grow the region.  
When the region growing is applied to the candidate region that is an oil slick (this is a True Positive, TP), the growing algorithm stops because the region is bounded by an edge pixel in which the gradient is high in several directions while other pixels within the region have similar grayscale values because they represent the homogeneous dark spots. On the contrary, the growing algorithm grows throughout the image when the candidate region is not an oil slick (this is a False Positive, FP). The FP is a consequence of imprecisions in the method with which to eliminate the noise zones in a precise manner, shown in Section 3.1.  
Finally, our method is able to extract features from the pixels in the candidate regions (both the TP and the FP) in order to determine the physical features of the oil slick for the TP and to ensure that the FP is discarded. It is possible to compute the area (pixels within a dark spot representing the candidate region), perimeter (contour pixels), elongation, bounding box, etc. This matter is not, however, addressed in this work.