Poster Presentation "Generation of High Resolution DSM Usin UAV Images"

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GENERATION OF HIGH RESOLUTION DSM USING UAV IMAGESIntroductionConclusion Post Processing of UAV images are better supported by algorithms from Computer Vision: SIFT Algorithm for feature extraction ,Dense Stereo Matching for Image Matching etc. Among the range of available commercial software packages , PIX4D provides more options for optimizing the results. For areas with smaller spatial coverage , UAVs images provides best data to generate high resolution photogrammetric products.Project Member: Uttam Pudasaini (011809-10) , Biplov Bhandari (011793-10), Niroj Panta (011807-10), Upendra Oli (011806-10)Project Supervisor: Mr. Uma Shankar Panday Project Co-supervisor: Asst. Prof. Nawaraj Shrestha LMTCKURecommendations High end work station is required for processing large number of UAV images through new algorithms. Make use of well distributed and accurately measured check points for assessing the accuracy of DSM. Object oriented image analysis techniques can be explored for increasing the accuracy of final DSMLPS-PIX4D LPS-AgiSoft PIX4D-AgisoftClassical Photogrammetric Workflow Computer Vision WorkflowParticularsAerial Photogrammetry UAV PhotogrammetryData Acquisition Manual/Assisted Assisted/Manual/ AutomaticAerial Vehicle Highly stable specially designed aircraftsSmall aerial Vehicles with certain payload capacityGPS/INS Configurationscm-dm level accuracycm-10 mImage Resolution cm-m mm-mGround Coverage Km2 m2-km2Cameras Well calibrated cameras especially designed for photogrammetric applicationsCan work with normaldigital camerasFudicial Marks Present AbsentFlying Height 100 m-10 km m-km(not more than 1 km)Data Processing WorkflowsStandard Photogrammetric WorkflowNo standard workflows Salient Feature Better control over the output image qualityHigh temporal accuracy with real time applicationsDigital Surface Models (DSM): Digital representation of theearths surface elevationincluding natural and artificialobjects like trees or buildingabove it.UAV Photogrammetry: Provides a low cost photogrammetric platform. An emerging field that canprovide very high resolutiondatasets for small areas. Remotely or (semi) autonomously controlled without human pilot.Among the range of terrestrial and aerial methods available to produce high resolution datasets, this project tests the utility of images acquired by a fixed wing, low cost UnmannedAerial Vehicle (UAV) by making use of image processing algorithms ranging from classical photogrammetry to modern Computer Vision (CV) algorithms.The effort and the achievable accuracy of DSM resulted from every process are compared using the highly accurate ground control points as the reference data. The comparison of theDSM is performed through difference of DSM, RMSE and visual interpretation. Although three software: LPS, AgiSoft PhotoScan and PIX4D were used for image processing, theidentified algorithms and limitations in processing are valid for most other commercial photogrammetric software available on the market.ObjectivesMain Objective To create a high resolution DSM using images acquired by a digital camera mounted in a UAV platform.Software UsedSub-Objectives To orient and georeference UAV images using internal and external orientation parameters. To generate Digital Surface Model. To compare and analyze the accuracy of DSM generated from different methodsData Used27 high resolution images acquired by a Trimble UX5 Imaging Rover (2.4 cm average spatial resolution) Control Points (GCP +Check Points)Camera Calibration parameters: Focal Length, Pixel Size and Distortion ParametersAbstractLarger spikes on output DSM from LPS.Classical Photogrammetric image matching algorithms fails for areas with homogenous and repetitive pattern. Poor results at area covered with trees and vegetation. For mixed topography, all the algorithms works fine.PIX4D provided the best result in all cases..Visual Interpretation and AnalysisPoint No Elevation (m) Elevation Difference (cm) Original (O) DSM LPS (a) DSM AP (b) DSM PIX4D (c) O-a O-b O-c 2003 136.173 136.278 136.116 136.201 -10.54 5.66 -2.84 2004 128.362 128.392 128.422 128.375 -3.04 -6.04 -1.34 2006 132.402 132.262 132.381 132.382 13.960 2.06 1.96 2007 127.585 127.649 127.653 127.571 -6.44 -6.84 1.36 2010 131.953 132.052 131.941 131.962 -9.94- 1.16 -0.94 RMSE(cm) 9.546 4.917 1.813 Mean= 8.783 Mean= 4.348 Mean= 1.688 RMSE Computation Difference of DSMDSM generated from LPS DSM generated from PIX4DDSM generated from AgiSoftDSM GenerationAerial Imagery Camera ParametersImage MatchingGeoreferencingInterior OrientationExterior OrientationAerial TriangulationBundle Block adjustmentInterpolationDSM GenerationUAV-acquired Imagery a. Orientation Parametersb. GCPs1. Initial ProcessingImage Matching(Between Images)Automatic Aerial TriangulationBundle Block adjustmentImage by image Key point ExtractionDensified Point Cloud2. Point Cloud Densification Filtered Point Cloud3. DSM GenerationAerial Photogrametry VS UAV Photogrametry