scholarly journals Least Squares Compactly Supported Radial Basis Function for Digital Terrain Model Interpolation from Airborne Lidar Point Clouds

2018 ◽  
Vol 10 (4) ◽  
pp. 587 ◽  
Author(s):  
Chuanfa Chen ◽  
Yanyan Li ◽  
Na Zhao ◽  
Bin Guo ◽  
Naixia Mou
Keyword(s):  
Radial Basis Function ◽  
Least Squares ◽  
Basis Function ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Compactly Supported ◽  
Terrain Model ◽  
Digital Terrain ◽  
Radial Basis

scholarly journals Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters

2019 ◽  
Vol 11 (9) ◽  
pp. 1037 ◽  
Author(s):  
Shangshu Cai ◽  
Wuming Zhang ◽  
Xinlian Liang ◽  
Peng Wan ◽  
Jianbo Qi ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Parameter Tuning ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Terrain Model ◽  
Adaptive Parameter ◽  
Digital Terrain ◽  
Airborne Lidar Data

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

scholarly journals High-Resolution Terrain Modeling Using Airborne LiDAR Data with Transfer Learning

2021 ◽  
Vol 13 (17) ◽  
pp. 3448
Author(s):  
Huxiong Li ◽  
Weiya Ye ◽  
Jun Liu ◽  
Weikai Tan ◽  
Saied Pirasteh ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Total Error ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Type I ◽  
Type Ii ◽  
Terrain Model ◽  
Digital Terrain ◽  
Airborne Lidar Data

This study presents a novel workflow for automated Digital Terrain Model (DTM) extraction from Airborne LiDAR point clouds based on a convolutional neural network (CNN), considering a transfer learning approach. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and interpolation. First, each point is transformed into a featured image based on its elevation differences with neighboring points. Then, the feature images are classified into ground and non-ground using ImageNet pretrained ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Last, the extracted ground points are interpolated to generate a continuous elevation surface. We compared the proposed workflow with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progressive Triangulated Irregular Network Densification (PTD). Our results show that the proposed workflow establishes an advantageous DTM extraction accuracy with yields of only 0.52%, 4.84%, and 2.43% for Type I, Type II, and the total error, respectively. In comparison, Type I, Type II, and the total error for PMF are 7.82%, 11.60%, and 9.48% and for PTD 1.55%, 5.37%, and 3.22%, respectively. The root means square error (RMSE) for the 1 m resolution interpolated DTM is only 7.3 cm. Moreover, we conducted a qualitative analysis to investigate the reliability and limitations of the proposed workflow.

scholarly journals Automated Generation of Digital Terrain Model using Point Clouds of Digital Surface Model in Forest Area

2011 ◽  
Vol 3 (5) ◽  
pp. 845-858 ◽  
Author(s):  
Kande R.M.U. Bandara ◽  
Lal Samarakoon ◽  
Rajendra P. Shrestha ◽  
Yoshikazu Kamiya
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Forest Area ◽  
Surface Model ◽  
Digital Surface Model ◽  
Automated Generation ◽  
Terrain Model ◽  
Digital Terrain

scholarly journals Photogrammetric surveying forests and woodlands with UAVs: techniques for automatic removal of vegetation and digital terrain model production for hydrological applications

2019 ◽  
Vol 7 (1) ◽  
pp. 1-20
Author(s):  
Fotis Giagkas ◽  
Petros Patias ◽  
Charalampos Georgiadis
Keyword(s):  
Vegetation Type ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Aerial Images ◽  
Surface Model ◽  
Terrain Model ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Dense Point ◽  
Height Model

The purpose of this study is the photogrammetric survey of a forested area using unmanned aerial vehicles (UAV), and the estimation of the digital terrain model (DTM) of the area, based on the photogrammetrically produced digital surface model (DSM). Furthermore, through the classification of the height difference between a DSM and a DTM, a vegetation height model is estimated, and a vegetation type map is produced. Finally, the generated DTM was used in a hydrological analysis study to determine its suitability compared to the usage of the DSM. The selected study area was the forest of Seih-Sou (Thessaloniki). The DTM extraction methodology applies classification and filtering of point clouds, and aims to produce a surface model including only terrain points (DTM). The method yielded a DTM that functioned satisfactorily as a basis for the hydrological analysis. Also, by classifying the DSM–DTM difference, a vegetation height model was generated. For the photogrammetric survey, 495 aerial images were used, taken by a UAV from a height of ∼200 m. A total of 44 ground control points were measured with an accuracy of 5 cm. The accuracy of the aerial triangulation was approximately 13 cm. The produced dense point cloud, counted 146 593 725 points.

A Hybrid Method Using Response Surface and Pattern Search for Design Optimization

2005 ◽  
Author(s):  
T. Zhang ◽  
K. K. Choi ◽  
S. Rahman
Keyword(s):  
Radial Basis Function ◽  
Least Squares ◽  
Response Surface ◽  
Basis Function ◽  
Function Approximation ◽  
Least Squares Method ◽  
Order Approximation ◽  
Pattern Search ◽  
Moving Least Squares ◽  
Radial Basis

This paper presents a new method to construct response surface function and a new hybrid optimization method. For the response surface function, the radial basis function is used for a zeroth-order approximation, while new bases is proposed for the moving least squares method for a first-order approximation. For the new hybrid optimization method, the gradient-based algorithm and pattern search algorithm are integrated for robust and efficient optimization process. These methods are based on: (1) multi-point approximations of the objective and constraint functions; (2) a multi-quadric radial basis function for the zeroth-order function representation or radial basis function plus polynomial based moving least squares approximation for the first-order function approximation; and (3) a pattern search algorithm to impose a descent condition. Several numerical examples are presented to illustrate the accuracy and computational efficiency of the proposed method for both function approximation and design optimization. The examples for function approximation indicate that the multi-quadric radial basis function and the proposed radial basis function plus polynomial based moving least squares method can yield accurate estimates of arbitrary multivariate functions. Results also show that the hybrid method developed provides efficient and convergent solutions to both mathematical and structural optimization problems.

scholarly journals 3D-Modelling of Charlemagne’s Summit Canal (Southern Germany)—Merging Remote Sensing and Geoarchaeological Subsurface Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1111 ◽  
Author(s):  
Johannes Schmidt ◽  
Johannes Rabiger-Völlmer ◽  
Lukas Werther ◽  
Ulrike Werban ◽  
Peter Dietrich ◽  
...  
Keyword(s):  
Cross Sections ◽  
3D Model ◽  
Digital Terrain Model ◽  
Airborne Lidar ◽  
Engineering Construction ◽  
Terrain Model ◽  
Digital Terrain ◽  
Danube Catchment ◽  
The Difference ◽  
Airborne Lidar Data

The Early Medieval Fossa Carolina is the first hydro-engineering construction that bridges the Central European Watershed. The canal was built in 792/793 AD on order of Charlemagne and should connect the drainage systems of the Rhine-Main catchment and the Danube catchment. In this study, we show for the first time, the integration of Airborne LiDAR (Light Detection and Ranging) and geoarchaeological subsurface datasets with the aim to create a 3D-model of Charlemagne’s summit canal. We used a purged Digital Terrain Model that reflects the pre-modern topography. The geometries of buried canal cross-sections are derived from three archaeological excavations and four high-resolution direct push sensing transects. By means of extensive core data, we interpolate the trench bottom and adjacent edges along the entire canal course. As a result, we are able to create a 3D-model that reflects the maximum construction depth of the Carolingian canal and calculate an excavation volume of approx. 297,000 m3. Additionally, we compute the volume of the present dam remnants by Airborne LiDAR data. Surprisingly, the volume of the dam remnants reveals only 120,000 m3 and is much smaller than the computed Carolingian excavation volume. The difference reflects the erosion and anthropogenic overprint since the 8th century AD.

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