A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

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.

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

“…Photogrammetric point cloud classification and the generation of an nDSM layer from aerial images is challenging in comparison with LiDAR data classification (without the first and last echoes), but not impossible; nowadays several specific algorithms are available [44,47,48]. However, only the elevation data is not sufficient to improve classification accuracy: distinguishing similar height parameter objects (e.g., building and vegetation) is a complex problem, and the combination of elevation attributes (slope, aspect, relative height) and surface parameters (texture and spectral information) can ensure a more accurate result [7,49]. Photogrammetry is less accurate compared to LiDAR, but its accuracy allows the applications in urban monitoring tasks using data fusion in classification methods.…”

“…In many cases, studies applied LiDAR as input data [35,56]; although, laser scanning data are often combined with other input data [19,30,38,57]. Several studies exploited the characteristics of returning laser pulses: first and last echo, intensity, point density, and geometry attributes [7,34,58,59]. Building detection also can be performed with aerial images; the capabilities of digital image processing are widely used to analyze urban areas, using image thresholding, filtering methods, and morphological operations [60].…”

Editorial for Special Issue: “Remote Sensing based Building Extraction”

“…The multi-source data-based methods can use the same types of data, e.g., panchromatic band and multispectral imagery [7], optical imagery and light detection and ranging (LiDAR) data [4].Recently, the rapid development of DNNs has been focused in remote sensing, and the networks have achieved remarkable progress in image classification and segmentation tasks [11]. The majority of the articles published in the Special Issue propose classification based on the DNN [1][2][3][4][5][6]8,[11][12][13].There are also a small number of methods based on segmentation [6] and morphological filtering [15].Using aerial LiDAR data, Awrangjeb et al [16] introduce a new 3D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. This method then uses the generated building models to detect 3D changes in buildings.…”

“…Using aerial LiDAR data, Awrangjeb et al [16] introduce a new 3D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. This method then uses the generated building models to detect 3D changes in buildings.…”

“…Among the methods that integrate data from multiple sources, Lai et al [16] apply a particle swarm optimization algorithm for building extraction based on the fusion of LiDAR point cloud and texture features from the elevation map which is generated from the LiDAR point cloud. Ying et al [1] combine the optical imagery and LiDAR data in a robust classification framework using the convolutional neural networks (CNN) and active contour model (ACM) to overcome the current limitations (e.g., salt and pepper artefacts) in algorithms for building boundary extraction.…”