Based On Satellite Imagery Hypothesized Digitized City Modeling

With the current remote sensing technology and computer technology development, remote sensing image has become the important data sources of geographic information. Compared with the traditional method, remote sensing image for information has the advantages of timeliness periodic, economic and other advantages. Using remote sensing satellite image to access roads and houses Geophysics information has become a concern of the countries around the world. Roads and buildings are important parts of digital urbanization, along with the development of the science and computer technology, quick automatic obtaining high quality and large network information of roads and housing has become possible. Through remote sensing satellite image extracting features information has become the research focus in the field of computer vision, computer image processing, and remote sensing and so on.This thesis research content involves image processing, pattern recognition and virtual reality. Some basic methods of digital image processing are discussed in the article, including image gray-scale melt, the binary image, image enhancement, the image smooth processing, the image edge detection, segmentation algorithm, histogram statistics, corrosion and expansive, patch statistics, etc.Specific research content:1. In the pretreatment stage, the methods to improve image quality are discussed, focusing on the strip noise removal, launched research with interpolation method, flourier transform method removing noise bands.2. On the basis of achieving road edge character, the existing edge detection operators and with curve fitting method of road edges fitting to realize road extraction are addressed in the article. This method for rural road extraction effect is better, but for the complex network of roads needs further research.3. On the basis of getting road contour through Image segmentation, using the template to detect the direction of the road is proposed. This method calculate the two attachment direction Angle based on the road start and end points, according to the direction of direction Angle choose corresponding template, test the starting point to the end point gradually, realize road extraction.This method requires multiple road start and end points, how to improve the degree of automation algorithm needs further study.4. Using image convolution operation to gain line features is researched, the coefficient can strengthen the way line features, also can weaken background gray constant area. Remove short line segment and massive noise, manual intervention for road clip, finally use mathematical morphology refining processing for getting road skeleton, and realize road extraction. Because the road usually connected with villages and towns, so the method needs to manually cut off the connection of roads and residents. If you can intelligently determine the off-road areas, and automatically cut away, then the automation of road extraction will be greatly improved.5. On the basis of basic outline of the road network by Image segmentation, proposed by Hough Line, the road again, road cutting, road connections, and the formation of the road network is determined to achieve the city straight road extraction. The line for urban road extraction method is better, but the algorithm robustness needs to be strengthened.6. The building contour detection algorithm based on probability is proposed.In this algorithm, cut-fusion algorithm for contour extraction and merge is proposed, the establishment of a probability model to measure the contour area is a possibility of the building. This algorithm has the advantage of combining the various features of the building through probability models, and by learning from the prior data to determine the model parameters, meanwhile, the model also has good scalability, the new feature is easy to join. Compared to other building extraction algorithms, this algorithm has better robustness and scalability.7. Building change detection algorithm based on contour extraction is proposed. The main idea of this algorithm is to first find out the image at different phases of the building outline, and then we propose a probabilistic model comparing the corresponding changes in the building outline and give them the possibility of changes in the last according to the probability of the corresponding value of the building to determine whether they have changed. An example can be seen that the algorithm for general texture changes in the building has better detection capabilities, and the algorithm itself is less precision image registration, has strong robustness. Figure [15] reference [65]...