Although contemporary geospatial science has made great progress, spatial data fusion of vector and raster data is still a problem in the geoinformation science environment. In order to solve the problem, this paper proposes a method which merges vector and raster data. Firstly, the row and column numbers of the raster data, and the X, Y values of the vector data are represented by Morton code in the C++ environment, respectively. Secondly, we establish the the raster data table and the vector data table in the Oracle database to store the vector data and the raster data. Third, this paper uses the minimum selection bounding box method to extract the top data of the building model. Finally, we divide the vector and raster data into four steps to obtain the fusion data table, and we call the fusion data in the database for 3D visualization. This method compresses the size of data of the original data, and simultaneously divides the data into three levels, which not only solves the problem of data duplication storage and unorganized storage, but also can realize vector data storage and the raster data storage in the same database at the same time. Thus, the fusion original orthophoto data contains the gray values of building roofs and the elevation data, which can improve the availability of vector data and the raster data in the 3D Visualization application.This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W10-1127-2020 | © Authors 2020. CC BY 4.0 License.
Abstract. There are some problems in the visualization of 3D model of buildings at present, such as redundant texture data and large memory occupation of loading texture data, which pose challenges to the smooth loading of 3D building visualization. In this paper, we propose a method which using fractal quadtrees to simplify texture data and compress management. Firstly, this method uses the fractal self-similarity and the feature that the same texture has the same fractal dimension to screen all the textures, when the fractal dimension of multiple textures is within the threshold range. Radon transform will be performed on these textures to calculate their standard deviation and further simplify the texture. Secondly, we use fractal texture compression to create a multi-resolution texture data structure with quadtree structure, so that the building 3D model can achieve dynamic visualization of different scales. Finally, the parallel texture mapping of all faces of the 3D model of the building is implemented according to the texture calling rules. Experimental results demonstrate that the texture is managed by the method proposed in this paper, which shortens the loading time of texture and reduces the memory occupation by about 48.92%. Therefore, the method of texture simplification and compression proposed in this paper has a significant effect in the process of building 3D model visualization.
Abstract. Shadows are ubiquitous in high-resolution images, especially in urban regions where there are more serious shadow occlusions. In order to improve the detection effect of shadows, this paper analyzes the characteristics and properties of shadows in orthophotos, and proposes an orthophoto shadow detection method under artificial shadow. Firstly, the shadow modeling tool is used to calculate the shadow regions (i.e. artificial shadow) caused by the building obstructing the sun's rays. Secondly, the relaxation matching algorithm is extended by the position and the shape of the shadow polygon as characteristic constraints. The relaxation matching algorithm is extended by the position and shape as shadow polygon’s characteristic constraints. Thirdly, the feature constraint value is calculated which between the shadow polygons of the two images. The correlation coefficient is used to obtain the initial probability value of each shadow polygon in the orthophoto. Finally, the optimal solution is obtained by continuous correction and iteration of the initial probability value. The method performs an overall matching of the two images and obtains the position of the shadow regions of the buildings in the orthophoto image. Experiment shows that this method reduces the mismatch rate and improves the matching accuracy. This method can detect shadow regions of buildings in orthophoto quickly and efficiently.
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