Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Fast Road Network Extraction in Satellite Images Using Mathematical Morphology and Markov Random Fields | EURASIP Journal on Applied Signal Processing 2004 16 2503-2514 2004 Hindawi Publishing Corporation Fast Road Network Extraction in Satellite Images Using Mathematical Morphology and Markov Random Fields Thierry Geraud EPITA Research and Development Laboratory 14-16 Rue Voltaire 94276 Le Kremlin-Bicetre Cedex France Email Jean-Baptiste Mouret EPITA Research and Development Laboratory 14-16 Rue Voltaire 94276 Le Kremlin-Bicetre Cedex France Email Received 1 September 2003 Revised 27 August 2004 We present a fast method for road network extraction in satellite images. It can be seen as a transposition of the segmentation scheme watershed transform region adjacency graph Markov random fields to the extraction of curvilinear objects. Many road extractors which are composed of two stages can be found in the literature. The first one acts like a filter that can decide from a local analysis at every image point if there is a road or not. The second stage aims at obtaining the road network structure. In the method we propose to rely on a potential image that is unstructured image data that can be derived from any road extractor filter. In such a potential image the value assigned to a point is a measure of its likelihood to be located in the middle of a road. A filtering step applied on the potential image relies on the area closing operator followed by the watershed transform to obtain a connected line which encloses the road network. Then a graph describing adjacency relationships between watershed lines is built. Defining Markov random fields upon this graph associated with an energetic model of road networks leads to the expression of road network extraction as a global energy minimization problem. This method can easily be adapted to other image processing fields where the recognition of curvilinear structures is involved. Keywords and phrases Markov random field mathematical morphology pattern recognition
