Research On Automatic Recognition Algorithms Of Typical Man-Made Objects

At present, the image data are dramatically increased with the resolution of remote sensing imagery being higher. It has become an important issue how to timely acquire the useful information from abundant image data. The efficiency and precision of interpretation and recognition from large breadth remote sensing images with high resolution are not assured if the traditional manual methods are only applied. It has important value for assisting interpreters to accomplish imagery interpretation by recognizing automatically targets in remote sensing imagery with computers. In this dissertation, the recognizing and locating techniques for man-made targets such as bridges and cirlular target clusters are investigated based on existed ATR techniques. The target features and recognition methods of typical man-made objects in high resolution satellite imagery with large size and complicated background is deeply investigated, and some valuable results are achieved.The advantages and disadvantages of visual interpretation, automatic interpretation and interactive interpretation are discussed. The characteristics of data from high resolution remote sensing imagery are summarized and the necessity of developing aided interpretation is pointed out. The main shortcomings of recognition techniques of man-made objects in high resolution remote sensing imagery are presented.A knowledge-based recognition algorithm of bridge over rivers is presented in order to overcome the shortcomings of existed algorithms. Firstly, the repository of bridges over rivers is set up in terms of the function features, gray features, geometric features and spatial context features. Secondly, river areas are coarsely segmented based on fuzzy theory and clustering analysis. Moreover, a method of selecting regions of interest (ROI) is proposed in terms of the spatial relationship between rivers and bridges. With the method, sub-images are automatically obtained. Lastly, image segmentation, contour tracking and line fitting are implemented to extract candidate bridges in each sub-image and every candidate bridge is verified using priori knowledges of bridges. During the process of bridge recognition, priori knowledges have been used all the time to speed up detecting and recognizing. By analyzing the features of cirlular-target clusters in high resolution satellite remote sensing imagery, the recognition method of cirlular target clusters is presented based on circle detection techniques and spatial distribution relationship of circular objects. With the characteristics that circular objects always locate together in each target cluster, possible circular objects are firstly detected using circle detection technologies. Then, the regions of circular-target clusters are validated using the relationships of spatial distribution and quantity of possible circular objects. Cirlular target clusters are recognized by eliminating false circular objects. In order to rapidly extract circular-target culsters, an improved circle detection approach is proposed based on RCD (Randomized Circles Detection) algorithm, which has ameliorations in two ways of sampling pixel selecting and operation on pixels in evidence-collecting process. In addition, two new algorithms are presented to quickly detect circles. In the end, three algorithms are compared with one another by experiments.The experimental analyses show that the methods proposed in this dissertation for automatically recognizing bridges and circular-target culsters in high resolution satellite imagery with large breadth and complicated backgrounds are fast and effective. It has the important realism sense for interpreters to quickly interpret and screen out man-made objects.