The original intensity value recorded by terrestrial laser scanners is influenced by multiple variables, among which incidence angle and distance play a crucial and dominant role. Further studies on incidence angle and distance effects are required to improve the accuracy of currently available methods and to implement these methods in practical applications. In this study, the effects of incidence angle and distance on intensity data of the Faro Focus^3D 120 terrestrial laser scanner are investigated. A new method is proposed to eliminate the incidence angle and distance effects. The proposed method is based on the linear interpolation of the intensity values of reference targets previously scanned at various incidence angles and distances. Compared with existing methods, a significant advantage of the proposed method is that estimating the specific function forms of incidence angle versus intensity and distance versus intensity is no longer necessary; these are canceled out when the scanned and reference targets are measured at the same incidence angle and distance. Results imply that the proposed method has high accuracy and simplicity in eliminating incidence angle and distance effects and can significantly reduce the intensity variations caused by these effects on homogeneous surfaces.

The intensity value recorded by terrestrial laser scanning (TLS) systems is significantly influenced by the incidence angle. The incidence angle effect is an object property, which is mainly related to target scattering properties, surface structures, and even some instrumental effects. Most existing models focus on diffuse reflections of rough surfaces and ignore specular reflections, despite that both reflections simultaneously exist in all natural surfaces. Due to the coincidence of the emitter and receiver in TLS, specular reflections can be ignored at large incidence angles. On the contrary, at small incidence angles, TLS detectors can receive a portion of specular reflections. The received specular reflections can trigger highlight phenomenon (hot-spot effects) in the intensity data of the scanned targets, particularly those with a relatively smooth or highly-reflective surface. In this study, a new method that takes diffuse and specular reflections, as well as the instrumental effects into consideration, is proposed to eliminate the specular reflection effects in TLS intensity data. Diffuse reflections and instrumental effects are modeled by a polynomial based on Lambertian reference targets, whereas specular reflections are modeled by the Phong model. The proposed method is tested and validated on different targets scanned by the Faro Focus^3D 120 terrestrial scanner. Results imply that the coefficient of variation of the intensity data from a homogeneous surface is reduced by approximately 38% when specular reflections are considered. Compared with existing methods, the proposed method exhibits good feasibility and high accuracy in eliminating the specular reflection effects for intensity image interpretation and 3D point cloud representation by intensity.

Terrestrial laser scanners (TLSs) can provide accurate and high-resolution data by measuring the distances (ranges) between the scanned points and the scanner center using time-of-flight or phase-shift-based methods. Distance measurement accuracy is of vital importance in TLSs and mainly influenced by instrument mechanism, atmospheric conditions, scanning geometry, and target surface properties. In general, existing commercial TLSs can achieve millimeter precision. However, significant errors (centimeter and even decimeter levels) beyond the instruments’ nominal accuracy exist in distance observations for targets with highly reflective surfaces whose specular reflections are dominant because these reflections can increase the backscattered laser signal power considerably and cause further disorder in the echo detection and recognition by TLS photodetectors. Apart from distance, the intensity value derived from the backscattered signal and influenced by the same factors as that of the distance measurement errors is recorded by TLSs. A certain link exists between the two instrumental observations. In this study, the anomalous distance measurement errors caused by target specular reflections are explored. The different planar reflective targets scanned by a Faro Focus^3D 120 terrestrial scanner are used to experimentally investigate the relationship between the original intensity values and the distance measurement errors. Results imply that the distance measurement errors caused by specular reflections are not as erratic as they ostensibly seem. On the contrary, distance measurement errors are strongly related to the original intensity values. A polynomial can be established to empirically model the relationship between the original intensity data and the distance measurement errors. With use of the original intensity to compensate for the measured distance observations, the point cloud data accuracy can be improved by approximately 55.52%.

Terrestrial laser scanner (TLS) and airborne laser scanner (ALS) can effectively capture point clouds from side or top view, respectively. Registering point clouds captured by ALS and TLS provides an integrated data source for three-dimensional (3-D) reconstruction. However, registration is difficult between TLS and ALS data because of the differences in scanning perspectives, scanning area, and spatial resolutions. A new method that can achieve automatic horizontal registration with ALS and TLS data based on building contour features is proposed in this study. The key steps include horizontal and vertical registrations based on 2-D building outlines and ground planes in ALS and TLS data, respectively. First, the 2-D building outlines are extracted from both ALS and TLS data. Second, the horizontal registration is accomplished by using the four-point congruent sets method for initial registration and the global optimization method for refined registration. Finally, the ground surface in the same region of ALS and TLS data are fitted for vertical registration, and the average elevation difference between the corresponding ground planes is calculated as the translation parameter value in the vertical direction. The results indicate that the proposed method can successfully match ALS and TLS data with an accuracy of 0.2-m both in the horizontal and vertical directions.

Owing to the complex forest structure and large variation in crown size, individual tree detection in subtropical mixed broadleaf forests in urban scenes is a great challenge. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) is a powerful tool for individual tree detection due to its ability to acquire high density point cloud that can reveal three-dimensional crown structure. Tree detection based on a local maximum (LM) filter, which is applied on a canopy height model (CHM) generated from LiDAR data, is a popular method due to its simplicity. However, it is difficult to determine the optimal LM filter window size and prior knowledge is usually needed to estimate the window size. In this paper, a novel tree detection approach based on crown morphology information is proposed. In the approach, LMs are firstly extracted using a LM filter whose window size is determined by the minimum crown size and then the crown morphology is identified based on local G_i* statistics to filter out LMs caused by surface irregularities contained in CHM. The LMs retained in the final results represent treetops. The approach was applied on two test sites characterized by different forest structures using UAV LiDAR data. The sensitivity of the approach to parameter setting was analyzed and rules for parameter setting were proposed. On the first test site characterized by irregular tree distribution and large variation in crown size, the detection rate and F-score derived by using the optimal combination of parameter values were 72.9% and 73.7%, respectively. On the second test site characterized by regular tree distribution and relatively small variation in crown size, the detection rate and F-score were 87.2% and 93.2%, respectively. In comparison with a variable-size window tree detection algorithm, both detection rates and F-score values of the proposed approach were higher.

Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by Terrestrial laser scanning (TLS). Neighborhood selection, feature selection and extraction, and classification of points based on the respective features constitute the commonly used workflow of point cloud classification. Feature selection and extraction has been the focus of many studies, and the choice of different features has had a great impact on classification results. In previous studies, geometric features were widely used for TLS point cloud classification, and only a few studies investigated the potential of both intensity and color on classification using TLS point cloud. In this paper, the geometric features, color features, and intensity features were extracted based on a supervoxel neighborhood. In addition, the original intensity was also corrected for range effect, which is why the corrected intensity features were also extracted. The different combinations of these features were tested on four real-world data sets. Experimental results demonstrate that both color and intensity features can complement the geometric features to help improve the classification results. Furthermore, the combination of geometric features, color features, and corrected intensity features together achieves the highest accuracy in our test.