We propose a new building detection and description algorithm for lidar data and photogrammetric imagery using directional histograms, splitting and merging segments, and line segments matching. Our  In recent years, the increasing need for accurate threedimensional (3D) data of urban areas, and their continuous update has led to research efforts that aim to develop automatic or at least semiautomatic tools for the acquisition of such data. To satisfy the new demands, more automated methods that produce accurate geo-information are required to keep costs within reasonable bounds. With its high pulse frequencies, light detection and ranging (lidar) is a very valuable data source for the production of geo-information. For this reason, lidar plays an important role in both the automation of building detection and the creation of 3D topographical databases. Early researchers generally used three kinds of methods to detect and reconstruct buildings from lidar data and photogrammetry. The first technique is to use only lidar data, because the photogrammetry of the region that corresponds to the region of the lidar data maybe impossible to obtain. There have been several attempts to detect building regions from lidar data. The task has been solved by classifying the lidar points according to whether they belong to bare-earth, buildings, or other object classes. Morphological opening filters are used to determine a Digital Terrain Model (DTM) that can be extracted by subtracting object points from the Digital Surface Model (DSM). By applying height thresholds to the normalized DSM, an initial building region is obtained. This initial classification must be improved to delete vegetation regions. In Brunn and Weidner (1997) The second type of method is to use 2D or 3D information from photogrammetric imagery. Early researchers tried to extract feature-ground separation, but it is hard to separate building boundaries from other distracting lines, such as road boundaries, by using only perceptual information such as parallel lines and right-angled corners. Therefore, other information is used for building extraction. The most notable is the depth information from stereo by multiple images (Sun et al., 2005) . Kim and Nevatia (2004) proposed a method that automatically constructs the description of complex buildings from multiple images. The main difficulty in utilizing stereo information is that although range data can be generated from stereo analysis, its quality is not good enough to generate building hypotheses directly, since the roofs of many buildings lack sufficient texture for stereo processing. In addition, nearby trees of similar height also make the use of such range data difficult. The third type of methods uses both the lidar data and photogrammetric imagery. Since lidar and photogrammetric imagery each has unique advantages and disadvantages for reconstructing building surfaces, advantages of one method can compensate for disadvantages of the other method making it natural to combine the two methods. More specifically, intensity and height information in lidar data can be used with texture and boundary information in photogrammetric imagery to improve accuracy. Shenk and Csatho (2002) proposed feature-based fusion of lidar data and digital aerial images to obtain a better surface description than could be achieved by using only one of these data sources. Habib et al. (2005) proposed a method based on the registration of photogrammetric imagery and lidar data using linear features. Sohn and Dowman (2003)
REF extract initial building regions from the point clouds and merge them with the regions segmented from images to reach coarse roof edges.
Fusion of Lidar and Imagery for Reliable Building Extraction
Photogrammetric Engineering and Remote Sensing
Geography