Model fusion for road extractions from multisource satellite images

International audienceIn the field of pattern recognition from satellite images, the existing road extraction methods have been either too specialized or too time consuming. The challenge then has been to develop a general and close to real time road extraction method. This study falls in this perspective and aims at developing a close to real time semi-automatic system able to extract linear planimetric features (including roads). The major concern of this study is to combine the most efficient tools to deal with the road primitive extraction process in order to handle multi- resolution and multi-type raw images. Hence, this study brought along a new model fusion characterized by the combination of operator input points (in 2D or 3D), fuzzy image filtering, cubic natural splines and the A*algorithm. First, a cubic natural splines interpolation of the operator points is used to parameterize the A*algorithm. Cost function with the consequence to restrict the mining research area. Second, the heuristic function of the same algorithm is combined with the fuzzy filtering which proves to be a fast and efficient tool for selection of the primitive most promising points. The combination of the cost function and the heuristic function leads to a limited number of hypothetical paths, hence decreasing the computation time. Moreover, the combination of the A*algorithm and the splines leads to a new way to solve the perceptual grouping problems. Results related to the problem of feature discontinuity suggest new research perspectives in relation to noisy area (urban) as well as noisy data (radar images)