Automatic classification of point clouds obtained with different airborne sensors in UAV

One of the main objectives when compiling information from drones, is the generation of digital terrain models - DTM, which are what ultimately allow the creation of cartographic products reliably, efficiently, accurately, economically and quickly. In the present work, information obtained in Colombia is used in 3 types of terrain, with different varying conditions and characteristics. The data is taken with 2 different sensors, located in 2 fixed-wing drones (X8 skywalker with Sony Alpha camera a6000 24MP and UAVER Avian P with Sony RX1R II 42 MP). Information processing was performed and point clouds were obtained about the same area for executing a comparative iterative analysis, and obtaining the optimal parameters of: iteration angle, slope and distance of terrain iteration. A semiautomatic classification of point clouds was used, with 4 different treatments proposed by the authors, called classification MACROS and different DTM was generated. To analyze the behavior of the point clouds and check the accuracy, a control of dimensions in the field was made, the land was divided into 12 plots and the difference in elevation was calculated with a cloud of checkpoints, obtained manually. Finally, a completely randomized block experiment model is designed, created and tested.

[1]  P. Surový,et al.  Determining tree height and crown diameter from high-resolution UAV imagery , 2017 .

[2]  Marc Olano,et al.  Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure , 2015, Remote. Sens..

[3]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

[4]  Juha Hyyppä,et al.  Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables , 2013 .

[5]  K. N. Tahar.  AN EVALUATION ON DIFFERENT NUMBER OF GROUND CONTROL POINTS IN UNMANNED AERIAL VEHICLE PHOTOGRAMMETRIC BLOCK , 2013 .

[6]  Mary L. Cummings,et al.  Toward the Development of a Low-Altitude Air Traffic Control Paradigm for Networks of Small, Autonomous Unmanned Aerial Vehicles , 2015 .

[7]  Heung-Kyu Lee,et al.  Extraction of digital elevation models from satellite stereo images through stereo matching based on epipolarity and scene geometry , 2003, Image Vis. Comput..

[8]  Timothy A. Warner,et al.  Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering , 2017 .

[9]  Xiaohu Zhang,et al.  Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses , 2016, Remote. Sens..

[10]  Zuxun Zhang,et al.  SGM-based seamline determination for urban orthophoto mosaicking , 2016 .

[11]  Reiner Zimmermann,et al.  Measurement of Within-Season Tree Height Growth in a Mixed Forest Stand Using UAV Imagery , 2017 .

[12]  Enric Pastor,et al.  UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas , 2014, Remote. Sens..

[13]  Philippe Lejeune,et al.  Comparison of UAS photogrammetric products for tree detection and characterization of coniferous stands , 2017 .

[14]  K. Kraus,et al.  ADVANCED DTM GENERATION FROM LIDAR DATA , 2001 .

[15]  Markku Kuitunen,et al.  Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification , 2014 .

[16]  Bao Li,et al.  Robust normal estimation for point clouds with sharp features , 2010, Comput. Graph..

[17]  Norbert Pfeifer,et al.  Experimental evaluation of ALS point cloud ground extraction tools over different terrain slope and land-cover types , 2014 .

[18]  B. Koch,et al.  UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS – TREE STEM MAPPING IN OPEN STANDS IN COMPARISON TO TERRESTRIAL LASER SCANNER POINT CLOUDS , 2013 .

[19]  W. Barragán,et al.  AUTOMATIC GENERATION OF BUILDING MAPPING USING DIGITAL, VERTICAL AND AERIAL HIGH RESOLUTION PHOTOGRAPHS AND LIDAR POINT CLOUDS , 2016 .

[20]  Ping Wang,et al.  Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data , 2017 .

[21]  Ziyue Chen,et al.  Upward-fusion urban DTM generating method using airborne Lidar data , 2012 .

[22]  Juha Hyyppä,et al.  Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging , 2017, Remote. Sens..

[23]  William Barragán Zaque,et al.  Obtención de parámetros óptimos en la clasificación de nubes de puntos LiDAR, a partir de sensores aerotransportados , 2017 .

[24]  Lihua Xie,et al.  A survey on recent progress in control of swarm systems , 2016, Science China Information Sciences.

[25]  Domen Mongus,et al.  Computationally Efficient Method for the Generation of a Digital Terrain Model From Airborne LiDAR Data Using Connected Operators , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  C. Silva,et al.  Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest , 2017 .

[27]  Tamas Jancso,et al.  Comparison of Digital Terrain Models gained by different technologies , 2011, Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services.

[28]  C. Schmullius,et al.  Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective , 2017 .

[29]  Emmanuel P. Baltsavias,et al.  A comparison between photogrammetry and laser scanning , 1999 .

[30]  Pablo J. Zarco-Tejada,et al.  Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .

[31]  LIN Zongjian,et al.  UAV FOR MAPPING — LOW ALTITUDE PHOTOGRAMMETRIC SURVEY , 2008 .

[32]  Christopher D. Lippitt.  Perspectives from the Field: Remote Sensing from Small Unmanned Platforms: A Paradigm Shift , 2015 .

[33]  Terje Gobakken,et al.  Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System , 2017, Remote. Sens..

[34]  Joanne C. White,et al.  Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment , 2015 .

[35]  Aleksandra Maria Zietara.  Creating Digital Elevation Model (DEM) based on ground points extracted from classified aerial images obtained from Unmanned Aerial Vehicle (UAV) , 2017 .

[36]  Marco Dubbini,et al.  Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments , 2013, Remote. Sens..

[37]  Nicholas C. Coops,et al.  Updating residual stem volume estimates using ALS- and UAV-acquired stereo-photogrammetric point clouds , 2017 .

[38]  M. Pierrot-Deseilligny,et al.  A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery , 2013 .

[39]  Anita Simic Milas,et al.  Different colours of shadows: classification of UAV images , 2017 .

[40]  Adam J. Mathews,et al.  Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem , 2016, Remote. Sens..

[41]  Karsten Jacobsen,et al.  Generation and Validation of High‐Resolution DEMs from Worldview‐2 Stereo Data , 2013 .