Abstract
Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.
Highlights
Remote sensing is the acquisition of information about objects or phenomena without physical contact [1]
Since the high density of the experimental point cloud may result in a large amount of calculation, it was necessary to down-sample the data in order to reduce the amount of calculation
According to the density of the point cloud after down-sampling, the data areas were divided into Low-Density Region 1 (LDR 1), LDR 2, the medium-density region (MDR), High-Density Region 1 (HDR 1), and HDR 2
Summary
Remote sensing is the acquisition of information about objects or phenomena without physical contact [1]. As elevation map is a kind of 2D image obtained by projecting the point cloud onto 2D planes, and it can provide abundant texture features and has been utilized in the field of building extraction. Kang et al achieved the rendering of barren terrain by enhancing the geometric features of elevation maps and increased the number of landscape features, which was most suitable for rendering barren terrain or planet surfaces [21] He et al proposed to organize LiDAR point data as three different maps: dense depth map, height map, and surface normal map. Du et al used the gray level co-occurrence matrix (GLCM) features to obtain textures from an elevation map and combined them with point cloud information to achieve area and object-level building extraction, and the results suggested a good potential for large-sized LiDAR data [25].
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