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- MakaleYayıncıda açık erişim2021Automatic extraction of trees by using multiple return properties of the lidar point cloud
Ozdemir, Samed; Akbulut, Zeynep; Karsli, Fevzi; Acar, Hayrettin
Airborne laser scanning has been a valuable tool for forestry applications since it began to be used commercially. Thanks to the high 3D resolution provided by the Light Detection and Ranging (LiDAR) point cloud, it has provided great convenience in complex 3D modeling processes needed for forestry applications such as forest inventory, forest management, determination of carbon stocks and the characterization of biodiversity. LiDAR data provides a new dimension in forestry applications with its high 3D resolution and multiple return characteristics. The extraction of woodland areas from the LiDAR point cloud has great importance for automating the determination of tree heights, species and stand frequency which will be used for generating canopy height models (CHM). In this study, woodland areas in the urban scene were automatically extracted by using the multiple return properties of the LiDAR point cloud. The proposed approach consists of three major steps namely pre-processing, parameter calculation and k-d tree search for trees which were implemented in MATLAB. In the first step, multiple return points have been identified from the LiDAR point cloud, which will be then used to determine possible tree locations. Then, by using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, neighborhood relations among the multi return points which were extracted from the initial point cloud data, were formed and a rule-based filter was applied by taking advantage of neighborhood relations. In addition, the initial point cloud was filtered with the Cloth Simulation Filtering (CSF) algorithm to separate ground and non-ground points where non-ground points used to extract trees. In the second step, non-vegetation points were removed by applying a threshold based on the change of curvature and planarity parameters, which are derived from the filtered non–ground point cloud. In the last step, in order to extract trees, a k-d tree structure was created from the filtered non-ground points to find nearest neighbors of each multi return point within a given diameter in the k-d tree structure. In order to evaluate the accuracy of the approach, the extracted boundaries were compared with the manually digitized woodland boundaries from the true orthophoto of the study area using correctness, completeness and quality metrics.
- MakaleYalnız künye2018Automatic Building Extraction from Image and LiDAR Data with Active Contour Segmentation
Akbulut, Zeynep; Özdemir, Samed; Acar, Hayrettin; Karsli, Fevzi
Automatic building extraction is an important topic for many applications such as urban planning, disaster management, 3D building modeling and updating GIS databases. Its approaches mainly depend on two data sources: light detection and ranging (LiDAR) point cloud and aerial imagery both of which have advantages and disadvantages of their own. In this study, in order to benefit from the advantages of each data sources, LiDAR and image data combined together. And then, the building boundaries were extracted with the automated active contour algorithm implemented in MATLAB. Active contour algorithm uses initial contour positions to segment an object in the image. Initial contour positions were detected without user interaction by a series of image enhancements, band ratio and morphological operations. Four test areas with varying building and background levels of detail were selected from ISPRS’s benchmark Vaihingen and Istanbul datasets. Vegetation and shadows were removed from all the datasets by band ratio to improve segmentation quality. Subsequently, LiDAR point cloud data was converted to raster format and added to the aerial imagery as an extra band. Resulting merged image and initial contour positions were given to the active contour algorithm to extract building boundaries. In order to compare the contribution of LiDAR to the proposed method, the boundaries of the buildings were extracted from the input image before and after adding LiDAR data to the image as a layer. Finally extracted building boundaries were smoothed by the Awrangjeb (Int J Remote Sen 37(3): 551–579. https://doi.org/10.1080/01431161.2015.1131868, 2016) boundary regularization algorithm. Correctness (Corr), completeness (Comp) and accuracy (Q) metrics were used to assess accuracy of segmented building boundaries by comparing extracted building boundaries with manually digitized building boundaries. Proposed approach shows the promising results with over 93% correctness, 92% completeness and 89% quality.
- BildiriYalnız künye20163D shoreline extraction using orthophoto and LiDAR data
Bayram, Bulent; Demir, Nusret; Ogurlu, Mustafa; Reis, Hatice Catal; Seker, Dursun Zafer
Shorelines are complex ecosystems and vital socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are indispensable for many purposes such as coastline management, agriculture, decision support systems etc. Thus, rapid, up-to-date and accurate information are required to monitor the shorelines. In this study, the Kestel Dam Lake has been selected for evaluation and testing of the methods which will be used for the research project, about shoreline extraction with use of UAV-Based LIDAR data in the region of Terkos-Istanbul, which supported by TUBITAK (The Scientific and Technological Research Council of Turkey) with project nr. 115-718. Kestel Dam is located on the river of Kestel that is constructed for the irrigation purposes, built between 1983 and 1988. Irrigation capacity of dam is about 4077 ha of agricultural areas. The used dataset is LIDAR data which is provided by HGK (Turkish General Command of Mapping). In this study, two different methods are applied with use of LIDAR data, acquired in 2013. One method is mean-shift segmentation and the other is Particle Swarm Optimization Analysis (PSO). The results are both compared with manually created reference vector dataset which is created using orthoimage dataset. The results shows that the developed methods have potential to give success in the research project which will be performed in the region of Terkos which is the study area of the project.