Edge Restricted Fractal Net Evolution Approach

FNEA (fractal net evolution approach) is an effective multi-scale image segmentation algorithm, and is considered as the basis of object based image analysis. But it is difficult to use the segmentation result of FNEA for high resolution SAR(synthetic aperture radar) images due to speckle noise and low contrast. We propose the edge restricted fractal net evolution approach (eFNEA) which uses additional information including edge information, fractal feature, and aggregates by constructing heterogeneity rules to improve the segmentation effect. In this algorithm, exact edges are extracted using edge detection algorithm which is built in the edge detection and image segmentation(EDISON) system to restrict small scale region growing procedure. And the heterogeneity is computed by aggregating multiple features including edge regularity feature to remove broken edges and thus improve the segmentation effect. Two experiments are conducted to verify the validity of the algorithm. The results show that the algorithm performed reasonably well even when images contain weak edges or heavy noise. From this point of view, eFNEA is better than FNEA.