针对以上这些问题，论文利用多种方法充分挖掘影像中的三维细节信息，借助航空影像数据的真数字正射影像（True Digital Orthophoto Map, TDOM）、数字高程模型（Digital Elevation Model, DEM）、数字表面模型（Digital Surface Model, DSM）等产品以及关键地物的光谱，形状、纹理等特征信息，进行点云过滤，从而快速高效，准确地进行关键地物的识别提取。主要完成了以下内容：
|其他摘要||With the development of digital photogrammetry technology, it becomes more and more common to get high resolution aerial image data through using manned or unmanned aircraft mounted camera. It has become a focus and hot in photogrammetry and remote sensing, computer vision, and many other research fields how to do the identification, extraction, classification and other operations of the target feature from these high-resolution aerial imagery containing rich detail information ,which has a very important theoretical and practical significance. Buildings, as an important part of cities, have a very important significance in the urban planning and construction. At the present stage, most of the researches about building extraction are based on high resolution aerial image is based on two-dimensional remote sensing data, manual or semi-automatic extraction. This operation easily leads to many issues, such as a large amount of work, low efficiency and can not fully use the three-dimensional information contained in aerial images. As one of the main factors to measure the development of the city, the road is one of the most updated elements in the process of urban development. The development of the road will directly affect the layout of urban functional land. In order to extract road information from high resolution aerial images with rich detail information and make up the traditional method that is easy to be influenced by the surrounding environment, such as vegetation, vehicle and shadow in extracting road, which easily leads to the wrong extraction, none-extraction and blurry edge.
In order to solve above problems, the paper makes use of various methods to fully tap the three-dimensional image details that draws support from True Digital Orthophoto Map(TDOM), Digital Elevation Model(DEM), Digital Surface Model(DSM), and texture, spectrum, shape, background to filter out point cloud . In this way, we can accurately identify the key feature of extraction quickly and efficiently. The main novel points of this paper are described in the following:
(1) Point cloud filtering for key features. The DSM generated by high resolution aerial images contains not only rich image detail information, but also the noise. The paper makes full use of the DSM height information, as well as the relevant spectrum, texture, shape and other information, so as to filter out the noise and get a more detailed DSM model.
(2) Automatic recognition and extraction of buildings. The influence of vegetation on the extraction of buildings is the biggest, so we should filter out the vegetation firstly. According to the result of the central projection aerial building tilt and mutual occlusion phenomenon, the paper uses TDOM extracted by overlapping aerial images as a base map. Then we can get the DSM of buildings through point cloud filtering, as a height restriction to the extraction of buildings.
(3) Automatic extraction of roads. This paper draws support from TDOM, DEM, DSM, and texture, spectrum, shape, background to filter out point cloud of building and vegetation. When getting the road DSM of little noise as height limit, we can make use of object-oriented technology for urban road extraction.
After repeated experiments, the efficiency and robustness of the proposed technology and the methods are evaluated via comparative experiments and real practical applications.|