Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering ap- proaches for stripping off non-ground objects namely; Gaussian low pass fil- ter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger win- dow sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get conti- nuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and How to cite this paper: Asal, F.F.F. (2019)
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Abstract: Terrestrial LiDAR data can be used to extract accurate structure parameters of corn plant and canopy, such as leaf area, leaf distribution, and 3D model. The first step of these applications is to extract corn leaf points from unorganized LiDAR point clouds. This paper focused on an automated extraction algorithm for identifying the points returning on corn leaf from massive, unorganized LiDAR point clouds. In order to mine the distinct geometry of corn leaves and stalk, the Difference of Normal (DoN) method was proposed to extract corn leaf points. Firstly, the normals of corn leaf surface for all points were estimated on multiple scales. Secondly, the directional ambiguity of the normals was eliminated to obtain the same normal direction for the same leaf distribution. Finally, the DoN was computed and the computed DoN results on the optimal scale were used to extract leave points. The quantitative accuracy assessment showed that the overall accuracy was 94.10%, commission error was 5.89%, and omission error was 18.65%. The results indicate that the proposed method is effective and the corn leaf points can be extracted automatically from massive, unorganized terrestrial LiDAR point clouds using the proposed DoN method. Keywords: corn leaves, terrestrial LiDAR, cloud points, automatic extraction, crop growth monitoring, phenotyping, difference of normal (DoN), directional ambiguity of the normals
Abstract: In this study we analyzed a series of terrestrial LiDAR point clouds acquired over a cliff in Puigcercos (Catalonia, Spain). The objective was to detect and extract individual rockfall events that occurred during a time span of six months and to investigate their spa- tial distribution. To this end local and global cluster algorithms were applied. First we used the nearest neighbor clutter removal (NNCR) method in combination with the expectation- maximization (EM) algorithm to separate feature points from clutter; then a density based algorithm (DBSCAN) allowed us to isolate the single cluster features which represented the rockfall events. Finally we estimated the Ripley’s K -function to analyze the global spatial pattern of the identified rockfalls. The computations for the cluster analyses were carried out using R free software for statistical computing and graphics. The local cluster analysis allowed a proper identification and characterization of more than 600 rockfalls. The global spatial pattern analysis showed that these rockfalls were clustered and provided the range of distances at which these events tend to be aggregated.
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Due to object recognition accuracy limitations, unmanned ground vehicles (UGVs) must perceive their environments for local path planning and object avoidance. To gather high-precision information about the UGV’s surroundings, Light Detection and Ranging (LiDAR) is frequently used to collect large-scale point clouds. However, the complex spatial features of these clouds, such as being unstructured, diffuse, and disordered, make it difficult to segment and recognize individual objects. This paper therefore develops an object feature extraction and classification system that uses LiDAR point clouds to classify 3D objects in urban environments. After eliminating the ground points via a height threshold method, this describes the 3D objects in terms of their geometrical features, namely their volume, density, and eigenvalues. A back-prop- agation neural network (BPNN) model is trained (over the course of many iterations) to use these extracted features to classify objects into five types. During the training period, the parameters in each layer of the BPNN model are continually changed and modified via back-propagation using a non-linear sigmoid function. In the system, the object segmentation process supports obstacle detection for autonomous driving, and the object recognition method provides an environment perception function for ter- rain modeling. Our experimental results indicate that the object recognition accuracy achieve 91.5% in outdoor environment.
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In the second stage, each scanline is processed separately in three steps, as shown in Fig. 4. As mentioned above, if an n-channel Lidar sensor is used, we have n scan- lines in each frame. Generally, the scanlines are circular. First, each scanline is divided into smaller “level-2” lines (see “Dividing a scanline into level-2 lines” section). Each “level-2” line contains a list of consecutive points in a scanline. This division is dependent on the distance between each pair of consecutive points. In the second step, all level-2 lines are classified and labeled, and reduce the number of types of line from four to two (see “Classification and labeling of level-2 lines” section). Each line is then either a ground line or a nonground line. Lines in which all points have the ground label are ground lines, and those in which all points have the nonground label are nonground lines. In the third step, each level-2 line is considered in relation to the other level-2 lines in the horizontal direction, and update the labels of any abnormal level-2 lines. If the label of a level-2 line changes, the labels of all points on the line change accordingly.
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Delineation of homogeneous patches is possible when one or more characteristics of trees or stands (e.g., tree height, canopy cover, density) are considered in a forest ecosystem. However, stand Structural Variables (SVs) are relatively homogeneous across managed forests which may lead to better identification of HPs in such forests. As shown by recent studies, ALS data can describe three dimensional structure of forest stands to estimate SVs in order to identify HPs (Sverdrup-Thygeson et al. 2016; Alexander et al. 2017). If a starting point is set within the center of a sample plot with a predefined size and specific variables extracted from ALS data are assigned to the point, it is possible to find the adjacent parts of a forest stand that are not different, to some extent, from the starting point at different spatial scales. Thus, the closely connected parts to the starting point in terms of statistical similarities of tree variables may be considered as HPs. Following these steps, one can use ALS data to artificially “ extend ” ground sample plots to identify clusters of similar areas following the previously presented idea of forest stratification (Parker and Evans 2004; McRoberts et al. 2012). Moreover, Næsset (2005) showed that integration of ALS data and measurements on ground enables a proper calibration of that first set, so the application of a combination of both datasets guarantees higher accuracies.
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Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classi- fication methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.
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From the aforementioned studies, it can be concluded that although several methods have been proposed for filtering the LiDAR point clouds, a powerful method has not yet been developed to be able to eliminate all objects from the LiDAR data. Therefore, filtering the LiDAR point clouds can be known as an open problem in photogrammetry and remote sensing. In this study, we have proposed a novel method based on the slope and progressive window thresholding for filtering the LiDAR point clouds. Progressive windows include two windows, the first one removes the small non- ground objects such as shrubs, and the second window eliminates the large objects such as buildings. In addition, the slope between two neighbor pixels can remove high outliers and the edge of the buildings. According to the physical characteristics of the ground surface and the size of objects, the best threshold value is considered. In the following, the paper explains the basic procedure of this algorithm and presents results and analyses obtained from its implementation.
An analysis using the full DARDAR-1000 data set (rather than data within 1 day of Cape Grim lidar observations of Figure 10) indicates that DARDAR under- estimates the 0.2–1.0 km altitude clouds by (i) 3.1 times for liquid-topped single-layer clouds and (ii) 2.5 times for proﬁles with multiple cloud decks. Another source of uncertainty in the results is the fact that the Cape Grim lidar does not collect data continuously (see Figure 2). This could result in incor- rect determination of parameters such as the surface-based estimate of cloud fraction, as was found for a reduced data availability study over a midlati- tude site in the United States (Kennedy et al., 2014). We perform two types of statistical bootstrapping on the Cape Grim data set to investigate how instru- ment downtime may aﬀect our results. First, we randomly remove half of the Cape Grim proﬁles and rerun the analysis, which results in an underestimate of the 0.2–1.0 km single-layer clouds of 3.1 times. Second, we randomly remove half (three quarters) of the days on which lidar measurements were made at Cape Grim and ﬁnd the underestimate of single-layer clouds to be 3.2 times (2.7 times). The equivalent changes for random removal of half and three quarters of the days on which multiple cloud deck observations were made at Cape Grim are 3.1 and 2.0 times, respectively. Monthly surface-based cloud fraction decreases by up to 10% when either half or three quarters of the days on which Cape Grim lidar proﬁles were obtained are randomly removed. We conclude, based on these bootstrapping experiments, that the reported DARDAR underestimate of these low-level (0.2–1.0 km altitude) single-layer clouds of about 3 times is robust. We further conclude that the DARDAR underestimate of low-level clouds at times when multiple cloud decks are present is less robust and probably closer to 2.5 times, given the spread of the results obtained under diﬀerent bootstrapping experiments of ∼2 –3 times.
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In the BMLC method, a Bayesian Probability Function is calculated based on statistics computed from the inputs for classes established from the training sites. This clas- sification begins with computing statistics for user se- lected training sites of land cover classes and uses the results of the statistical summary to classify the image. Each pixel is judged as to the class to which it most probably belongs. Histogram analysis was performed to locate image clusters using intensity and distance metric . The 2001 National Land Cover Data (NLCD) Classi- fication Scheme was used when histogram analysis shows the existence of more homogenous regions within the classes resulted from applying the BMLC method. The classified orthoimage of the study area was then vectorized using a run graph method ; resulting in a polygon layer. This vectorization method groups pixels in the raster image into area fragments, which were re- fined using line fitting and line extending processes. Then, the vector layer was used to initiate a sweeping spatial clustering algorithm in order to identify clusters in the LiDAR dataset . Clustering is a well-studied subject and hence many algorithms already exist that can be categorized as hierarchical, partitioning-based, graph- based, density-based algorithms, model-based, and a few combinational algorithms.
There are always significant changes in vertical profiles of aerosol concentration, specific humidity, potential temper- ature or turbulence around the top layer of the ABL, mak- ing it possible to derive the BLH. There are several instru- ments used for the determination of BLH based on the sharp gradient in the vertical profiles mentioned above (Baars et al., 2008; Bonin et al., 2018; H. Li et al., 2017a; Seibert, 2000; Yang et al., 2017): for example, in situ instruments, such as radiosondes, balloons, masts, and aircraft, and re- mote sensing instruments, such as sodar, wind profilers, lidar, and ceilometers. All of these instruments have advantages and shortcomings regarding accuracy, detection range, and spatial and temporal resolution, as summarised by Seibert (2000). Among these instruments, the lidar system provides backscattering signal with a sufficient spatial and temporal resolution, a long enough detection range and high enough accuracy for determining the BLH. These qualities make li- dar a powerful tool for BLH assessment.
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Currently, there are technologies such as Unmanned Aerial Vehicles (UAV) that allow more efficient field work in geology. With the capture of aerial photographs and their processing with algorithms based in Structure from Motion (SfM), it is possible to obtain a position XYZ with their RGB color. The resulting outputs, whether images or point clouds, can be used to recognize patterns of geological layer in stratified walls from color. This semi-automatic segmentation allows to identify subtle changes between lithologies through the transformation of RGB to other color spaces such as CIELab.
is chosen such that in the data rich limit classifiers are binary valued. The motivation for our approach is to validate approximating the hard classification problem by a soft classification problem. The soft classification problem is in general numerically easier  and therefore more appealing to the practitioner. However one also wants to be precise in regards to which class a data point belongs. Minimizers of the Ginzburg-Landau functional are used as a classification tool  in order to allow for phase transitions which allow a soft classification approach whilst also penalizing states that are not close to a hard classification.
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Using (1) and (2) in several groups of data, which can be calculated by the radius of features, so as to search two point cloud layer difference ξ , then use the matching angle features ultimately determine whether they are in the corresponding layer can be matched.
The ability to perform a rapid and dense measurement of huge amounts of object points is itself, an overriding advantage of TLS in comparison to other sensor technologies and point-wise monitoring approaches, where deformation evaluation is limited to few discrete and well signalized points. When two sets of point clouds of the deformed object are obtained from different epochs, the data can be modelled separately and used for a deformation analysis, employing the available statistical methods and exploiting the high data redundancies of TLS.
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A 3rd order polynomial transformation is also a global transformation allowing evaluation of the errors associ- ated with each GCP. These error estimates could be used to evaluate the usefulness of each GCP. Locating points on the LiDAR involved a great deal of subjective ‘expert’ opinion as to permanence of old channel features, but an objective error estimate could be used to eliminate mistakes. The final 15 GCPs were located as depicted in Figure 3 and results of the 3rd order polynomial trans- formation. The 3rd order polynomial transformation (Geomap 3) met all of our criteria for a successful georef- erence of the 1885 map. The fifteen GCPs were evenly distributed with four on each map sheets one and two and six on the multiple sections of map sheet three. They also represented a minimum RMSE of 23.7 m (77.5 ft). The five GCPs used in producing Geomap 1 were also used for Geomap 3 but point 10 was the only landscape element that was consistent on both aerial photographs and LiDAR DEM. This suggests that aerial photointer- pretation is limited to cultural artifacts and landscape elements were better located on the DEM. This is not surprising for the Congaree National Park since most of the park is a closed canopy floodplain forest that has been undisturbed for decades.
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This paper presents a highly dense, precise and diverse labelled point cloud. Herein, an extensive manually annotated point cloud dataset is introduced for Dublin City. This work processes a LiDAR dataset that was unstructured point cloud of Dublin City Centre with various types of urban elements. The proposed benchmark point cloud dataset is manually labelled with over 260 million points comprising of 100’000 objects in 13 hierarchical multi- level classes with an average density of 348.43 points/m 2 . The intensive process of labelling is precisely cross-checked with expert supervision. The performance of the proposed dataset is validated on two salient applications. Firstly, the labelled point cloud is employed for classifying 3D objects using state-of-the-art CNN based models. This task is a vital step in a scene understanding pipeline (e.g. urban management). Finally, the dataset is also utilised as a detailed ground truth for evaluation of image-based 3D reconstructions. The dataset will be publicly available to the community.
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By discussing three 24 h case studies, it was shown that the aerosol discrimination is very feasible and informative and gives a good complement to the Cloudnet target cate- gorization. By analysing the entire HOPE campaign, almost 1 million pixel (5 min, 30 m) could be successfully classi- fied with the newly developed tool from the 2-month data set. We found that the majority of the aerosol trapped in the PBL were small particles as expected for a heavily populated and industrialized area. Large, spherical aerosol was found mostly at the top of the PBL and close to cloud bases, in- dicating the importance of hygroscopic growth of the parti- cles at high relative humidity. Interestingly, it was found that on several days non-spherical particles were mixed from the ground into the atmosphere. The origin of these particles re- mains unclear and needs further research. Lofted layers of Saharan dust as it is typical for spring in Germany were ob- served only sporadically and with low AOD during the in- vestigated time frame of the HOPE campaign in spring 2013. Non-typed aerosol with low concentrations was found often above the PBL up to heights of about 4 km. Cloudnet was not able to identify these optically thin particle layers due to the lower sensitivity of the used ceilometer. The capability to detect cloud bases was compared to the Cloudnet feature mask, and the good agreement gives evidence that this fea- ture could be used to apply robust cloud screening, which is often needed for lidar data retrievals, for example, for other automatic approaches such as the EARLINET Single Cal- culus Chain (D’Amico et al., 2015). Ice crystals were also often classified correctly but sometimes remained unclassi- fied or were even falsely classified as aerosol as a conse- quence of multiple reasons (a priori information aiming at aerosol, low depolarizing characteristics in certain tempera- ture ranges, etc.). This behaviour might be overcome when combining the lidar stand-alone target categorization with the Cloudnet target categorization as planned in ACTRIS-2 2 . Then, the 10 lidar-based target types are available in addition to the already existing Cloudnet quantities for an advanced categorization of both aerosol and clouds. In this way, errors, i.e. misclassifications, could be minimized in both schemes
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The critical component in this approach is the cost func- tion. A well-designed cost function produces low values for edges following structure or contact traces and high values for edges outside or cross-cutting traces. Our optimised im- plementation of Dijkstra’s algorithm then follows edges with the lowest cost values in order to map out the feature of in- terest. Conveniently, simple cost functions such as point or pixel brightness or local colour gradient work well on most geological datasets; the examples presented below all map a single scalar attribute in the dataset directly to cost (point or pixel brightness for study 1 and 2, topographic slope for study 3 and bathymetric depth and vertical gravity gradient for study 4). We have designed and implemented these and several other simple cost functions that give reasonable re- sults for different structure and data types. Specific equations for these are included in Appendix A.
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Even though the initial costs of the scanner and software license are high, terrestrial laser scanning (TLS) techniques are generally easier and more cost-efficiently applied to in- dividual sites and on the annual to seasonal timescale com- pared to ALS techniques (Heritage and Large, 2009). As of- ten nearly the entire surface of very small glaciers is visible from one single location (e.g. from a frontal moraine, an ac- cessible mountain crest or summit, or from the opposite val- ley side), TLS is particularly appropriate to generate high- resolution DEMs, as well as to derive annual geodetic mass balances of very small glaciers. Thus, laborious and time- consuming in situ measurements could be circumvented, and the spatial inter- and extrapolation of point measurements over the entire glacier surface avoided, which is known as an important source of uncertainty in direct glaciological mass balances (e.g. Zemp et al., 2013).
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