CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

Abstract

ABSTRACT:LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatialbased features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with 'Riegl LSM-Q680i' full-waveform laser scanner with the density of 16 points/m 2 . The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA

“…In point classification, each irregularly distributed 3D LiDAR point is assigned to a semantic object class which it is belongs to (Niemeyer et al, 2014;Charaniya et al, 2004). The features of all points in the LiDAR point cloud are used in automatic point-based classification applications and appropriate parameters of these features are usually determined as a result of training steps according to the targeted classes in the classification stage (Kim and Sohn, 2010;Mallet et al, 2011;Sohn, 2013, Yastikli andCetin, 2016). The used features in point based classification algorithms can be divided into 3 groups as spatial-based features, echo-based features and waveform-based features (Mallet et al, 2011).…”

“…Therefore, it is necessary to classify the LiDAR point cloud correctly in order to obtain highly accurate results in such studies (Charaniya et al, 2004). In classification steps of the LiDAR point clouds, each point is assigned to the meaningful classes such as ground, vegetation, building according to the characteristics of the LiDAR data (Yastikli and Cetin, 2016). Algorithms for classifying LiDAR point clouds can be grouped into two categories based on used data type: point clouds and raster range image.…”

3D City Model from LiDAR of Akoka, Lagos Using Geographical Information Technique

“…The first kind of algorithm is used on point clouds of LiDAR directly while the second kind works with raster images, to enable the irregularly distributed LiDAR point cloud to be gridded (Bao et al, 2008). The features of all points in the LiDAR point cloud are used in automatic point-based classification applications and appropriate parameters of these features are usually determined as a result of training steps according to the targeted classes in the classification stage (Kim and Sohn, 2010;Mallet et al, 2011;Sohn, 2013, Yastikli andCetin, 2016). In characterization ventures of the LiDAR point mists, each point is appointed to the significant classes, for example, ground, vegetation, working as per the qualities of the LiDAR information (Yastikli and Cetin, 2016).…”

A greyscale voxel model for airborne lidar data applied to building detection

“…This initially identifies seeds located on planar surface patches and then enlarges these patches around the seeds using smoothness constraints, curvature consistency or other similarity criteria (Morgan and Habib, ; Orthuber and Avbelj, ; Vo et al., ; Gilani et al., ; Wang et al., ). Segmentation . This method firstly segments lidar point clouds into individual processing units using local surface properties as a similarity criterion and then detects building units using building characteristics (Maas, ; Sithole and Vosselman, ; Wang and Tseng, ; Carlberg et al., ; Moussa and El‐Sheimy, ; Zhang and Lin, ; Zhou and Neumann, ; Bellakaout et al., ; Yastikli and Cetin, ; Zhang et al., ). Clustering . This method first associates each lidar point with a feature vector, which consists of geometric and/or radiometric measures and then segments lidar points in feature spaces by a clustering technique such as k ‐means, maximum likelihood or fuzzy clustering (Filin, ; Hofmann, ; Vosselman et al., ; Filin and Pfeifer, ; Sun and Salvaggio, ; Kong et al., ; Zhao et al., ; Song et al., ; He et al., ; Kim et al., ; Cao et al., ). Filtering .…”