Extracting Trees From Lidar Data In Urban Region

Airborne Light Detection and Ranging (LiDAR) is an active Earth Observation System (EOS), its development goes back to the 1970s and 1980s, with an early NASA system and other attempts in USA and Canada. It represents a new and independent technology. The 3D surface data can be acquired in low-cost,high-density,rapid,high-precision, to generate Digital Surface Model (DSM) automatically. Now, LiDAR has become a new technique in the survey field.As an important research content of the development of digital city, the extraction of city surface information signifies much for visualization. Though the LiDAR system can acquire the city surface information, the information has made up of different objects. It is difficult to realize city modeling if the surface information had not been processed. Therefore, the trees or buildings have to be represented separately on the terrain surface. At present, the research about the surface information extraction from LiDAR cloud data mainly focus on the building extraction and the tree extraction relatively lacks. Tree as an important part of city surface information, its visualization cannot be neglected. Therefor, extracting the tree from urban area in LiDAR data has much application value.At present, most of the methods to extract trees from LiDAR data are the course of segmentation and classification. We can use clustering or the usual algorithms of digital image processing to segment the LiDAR data, following, the trees may be separated from the segmented data by supervised classification or unsupervised classification. These algorithms, such as Secord's algorithm, have two-step methods for tree detection consisting of segmentation followed by classification. The segmentation is done by a simple region-growing algorithm with weighted features from aerial image and LiDAR. His algorithm's judging standard is based on the similarity between the points. Then, a feature vector is defined for each segment. The classification is done by weighted support vector machines (SVM). These methods do not fully consider the characteristic of the LiDAR data during the segmentation, and rather complicated. During the classification, they need select many high quality marked samples, and the calculation complications is high.This paper discusses the efficiency of the LiDAR data processing firstly. The efficiency of data processing can be greatly raised by building the spatial index for LiDAR data. Then the DSM characteristics are analyzed base on the segmentation and classification, and a tree extracting algorithm is presented from the LiDAR data of the first return pulse in complicated urban environment. This algorithm based on region-growing and gradient threshold. Finally using the corresponding image to evaluate the experimental results, the tree extracted ratio is 85.9% and its accurate ratio is 86.8%. The experimental results show that new algorithm can extract trees accurately from LiDAR data in urban region. Analyzing the tree extraction results, there have some abnormal points on the roof that had been mistaken as tree points. This is the obvious mistake. So, this algorithm needs to improve, and gray open operation is implemented to remove the abnormal points on the building roof. Then the region-growing and gradient threshold value is used to extract trees. After improved, the tree extracted ratio is 86.3% and the accurate ratio is 87.9%. Compare with the original algorithm, besides there is a slight improvement in the tree extracted ratio and its accurate ratio, what is more important the improved algorithm can avoid the obvious mistake that the abnormal points are taken as tree points.