Building Extraction Based On The LIDAR Point Cloud Data And Image Fusion

The concept of digital city is put forward with the rapid development of information technology in recent years. The demand of three-dimensional city model which is the center content of the current of digital city is growing in many industries, such as urban design, land management, communications industries, transportation, water conservancy, fire power, city networks. The three-dimensional reconstruction and visualization of city buildings is the emphases and difficulties of establishing three-dimensional city model. The major objectives of three-dimensional reconstruction of the city building is efficiency, economy and establishing three dimensional models in sufficient detail accurately proceeding from the economy and adaptability. With the continuous development and wide application of airborne LIDAR technology, building model reconstruction based on LIDAR point cloud data technology becomes more and more valuable.Though LIDAR data and image data have their own advantages and disadvantages, they can complete each. You can get three dimensional geodetic coordinates of the surface objects directly, largely, quickly with LIDAR systems, but it was not enough to get detail information such as the texture of an object. It is very difficult to extract the buildings with LIDAR point cloud data directly. There are rich texture detail information in ground object information obtained from the image data, but using images directly to obtain the ground objects coordinates of points is also a very difficult issue. Thus, it has important practical significance that using precise three dimensional coordinates of point cloud data by LIDAR technologies and rich texture and image information to classify, extract, establish three-dimensional city model.It extracts urban buildings by combining DSM depth imaging with LIDAR point cloud data on the basis of summarizing the results of research of the predecessors in the article. First of all, this article manages the storage of LIDAR data using two-level grid, and uses the interpolation method for LIDAR point cloud o generate DSM depth imaging. Secondly, we will take the DSM depth image for image transforms, image, binary, and so on with image processing technology. We will extract the edge of buildings through the image edge detection technology on this basis. Then we can select the desired points with the same name that DSM depth imaging and LIDAR point cloud data by labeling method. With the help of a number of points with the same name selected, it uses adjustment theory and DSM depth imaging LIDAR point cloud data to get matching parameters to establish matching relations between the two. Finally, we can extract out LIDAR point cloud data that corresponds to the edge of the building roughly by the relationship of DSM depth image edges and extracted buildings with matching LIDAR point cloud data.On this basis, using slope difference method and regional growth to extract LIDAR point cloud data structures precisely. Main content of this article and the results are as follows:(1) LIDAR data preprocessing:Analyzing the characteristic of LIDAR data, it describes several common storage methods of the data briefly and summarizes the advantages and disadvantages of each approach. On this basis, this article processes the original LIDAR data by improved slope filters and removes the gross error of the LIDAR data. This article generates DSM with interpolation method and change LIDAR data generated into corresponding DSM depth images by DSM ash measurement based on elevation information from LIDAR data in order to performance information of experimental regional surface better.(2) DSM depth image preprocessing:Because of the use of DSM depth image generated to extract image outlines of buildings in this article, so we should need the help of image processing technology to preprocess DSM depth image. In order to expand the image contrast and character changes more apparent, this article uses piecewise linear transformation to do gray scale transform on the DSM depth image first of all, and then uses the method of Outs to do binarization processing on the DSM depth image. The image is roughly divided into building and non-building area and then uses Canny edge detection to extract the range of buildings more or less.(3) Because the method in this article matches roughly on DSM depth imaging and LIDAR data matching which matching accuracy is not high, so after the presentation of traditional image matching algorithm, it proposes a method of matching algorithm which is relatively simple. Firstly it uses labels to select the required number of points with the same name when matching DSM depth imaging and LIDAR data. Then striking a match between arguments by the selected points with the same name.At last, we can establish the corresponding of DSM depth imaging and LIDAR data by the parameters. The algorithm of the method is simple, which is easy of programming implementation. It improves the efficiency of matching LIDAR data and the DSM depth images.Extracting building by LIDAR accurately. You can extract the edge of buildings using DSM depth imaging on the basis of matching DSM depth imaging and LIDAR data. You can determine the approximate range of LIDAR data structures through the established matching relations between the two. However, we must process the range extracted to extract outlines of buildings accurately. This paper presents that picking out the remaining non-buildings of LIDAR data by the method of gradient difference and, extracting the building outlines precisely by the method of leverage regional growth. In this paper, research results are as follows:(1) On the basis of the traditional rules of grid LIDAR data organization based on two grids, the algorithm is relatively simple, easy to program, and improve the efficiency of data processing.(2) In this paper, the labeling method for the selection of the same name point to avoid the error of using the mouse to interoperate select the same name point to bring directly to the establishment of DSM depth images and LIDAR data points matching relations can be largely improved the accuracy of the same name point to select,to ensure the accuracy of image matching, and the algorithm is relatively simple, easy to program, to achieve the automation of image matching.(3) Access to the region of image building, but also to obtain the corresponding LIDAR data building area, and a vector data base for follow-up3D model as well as other fields of application.(4) Using VC++to complete "the development of the LIDAR data to extract building software systems".