Research Of Methods To Extract Road Network From SAR Images And Update Spatial Data In GIS

The update of spatial data has become a serious problem confronted by the database of geographical information system all over the world. With the development of the technique of remote sensing, especially the launch of the SAR remote sensors, remote sensing, as a tool to update the spatial data, is becoming more and more important. Currently, the road network extraction from the remote sensing images, especially from the high-resolution SAR images, has become a hotspot in remote sensing application research. And the goal is to offer viable and cost-effective approaches for road centerline delineation and for revision of spatial databases using automated or semi-automated extraction techniques. Based on the analysis and summarization of the research home and abroad, the dissertation makes deep research on the methods to extract road network from SAR images and to update the spatial data of GIS.In high-resolution SAR images, rich details and the complicated background of objectives, along with the intrinsic speckle, make it difficult to extract road network directly from original SAR images. Aiming at this problem, fuzzy C means is first used to classify the SAR images to extract road pixels, and then GA or Snakes to extract road centerline according to different mathematical models. The dissertation presents two semi-automated methods to extract road network from high-resolution SAR images. The experimental results show that the method based on GA bears higher accuracy, but the computing time is a little longer, while the speed of the method based on Snakes is faster, but the accuracy falls.The contrast of road in SAR images with the background is tightly related with the side-looking direction of radar and the extension direction of roads. Aiming at this problem, a multi-threshold detector is presented. The road pixels detected by the local detectors form into road segments of the corresponding direction by using curve fitting. And a data structure of graph whose vertices are the extracted road segments is constructed for road network to organize the segments effectively.Because of the uncertainty existing in the relations (such as proximity) between the road segments, a method to connect road segments using fuzzy inference system is presented. In this method, there are three steps ?Collinearity connection, segment mergence and junction connection. And each step includes a fuzzy inference system of Mamdani type. The method possesses very preferable adaptability. If only fuzzy rules selected rationally, the fuzzy inference system can connect segments into road network.Because of the redundant and complementary information existing in different sensor data from the same areas, it can reduce the uncertainty and improve the accuracy of the extracted features by fusing multi-sensor data. In order to make full use of all kinds of sensor data to extract road network more exactly, the dissertation presents a method to extract road network by fusing multi-sensor data. During the local detection, aiming at the features of radar images and optical images, the respective line detectors are devised based on the thought of Dudo Road Operator (DRO), and they can detect road pixels at any direction. The Dempster-Shafer evidence theory is used to fuse the detected results of different sensor images such as SAR images and TM images. After the road pixels obtained by fusion being thinned and fitted linearly, the road segments are connected conditionally by using the fuzzy reasoning system that fuses some prior knowledge. The result shows that both the completeness and correctness of the road network obtained by the fusion method are sharply higher than that of the road network extracted only from SAR images. And the quality of the road network extraction algorithm presented in this dissertation is evaluated.Finally, the dissertation makes research on the method to update road network data of GIS by using remote sensing images, and presents an algorithm for linear features to detect changes. Taking each road feature in the...