C.M.F. Pereira1, E.C. Riqueza2, F.L. Melo3, O.R. Vergara1

1 - IME, Cartographic Engineer Department, Rio de Janeiro, Brazil

2 - IME, Chemistry Engineer Department, Rio de Janeiro, Brazil

3 - CTEx, Tecnologic Center of Army, Rio de Janeiro, Brazil



Accidental pollution causes the launching of a great polluting mass in the environment, resulting on considerable damages. Among many types of accidental pollution, those involving oil spills requires a great concern because of its fast dispersion. Although difficult to be evaluate, the definition of the accident localization and magnitude becomes a very important. Due to this complexity, this paper proposes a methodology for using measures of texture and deformable models in order to identify and quantify oil-spot areas. A new image segmentation algorithm named G-Snakes was developed, based on. deformable models, known as geodesic active contours, that consist in deforming an initial curve to identify and extract the contours of objects of interest in images. The proposed algorithm intends to minimize inconsistent results, such as over-segmentation of homogeneous areas, generation of false boundaries and open contours. These incoherent results are usually associated to traditional methods of segmentation such as clustering and region growing. Another advantage of the implemented algorithm is its outputs of cartographic features boundaries in vector format, which makes possible any further necessary edition of these data. Considering the area features, the implemented algorithm produces not only the feature contour, but also computes its surface. As a study case, this paper analyzes an oil spill in Guanabara Bay, Rio de Janeiro, Brazil occurred in January 18th, 2000. The methodology for the oil-spot area extraction consists on three tasks: 1) Processing of a SAR image with textures filters, for speckle attenuation; 2) Applying the G-Snakes algorithm in the SAR processed image for the oil-spot segmentation; 3) oil-spot area calculation. This methodology proved to be efficient for performing a quick evaluation of the disaster, and for supplying information about localization, shape and dimension of the oil-spot. These parameters are essential for monitoring any kind of environmental disaster.