dsm generation using high resolution uav images

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  • 1.Group Members Biplov Bhandari Upendra Oli Niroj Panta Uttam Pudasaini Project Supervisor Uma Shankar Pandey Co-Supervisor Mr. Nawraj ShresthaFriday, May 23, 2014 1

2. INTRODUCTION DSM(Digital Surface Model ) : Raster model representing the elevation of earth surface with natural and artificial objects on it. Applications: Viewpoint selection Line of sight analysis Urban planning Flood modeling and simulation Data Acquisition: HOW??? Friday, May 23, 2014 2 3. Digitizing existing topographical maps Field survey (GPS, Total Station) Laser Scanning` RADAR LIDAR Friday, May 23, 2014 3 Cost and Time ineffective Suited for the area having large spatial extent UAV 4. UNMANNEDAERIALVEHICLE(UAV) New photogrammetric measurement tool Evolving as low-cost alternatives to the classical manned aerial photogrammetry and high cost Satellite Imagery for areas with smaller spatial extent Popularity is increasing day by day because of its features like Can carry sensors (optical as well as others) and help to conduct photogrammetric works and many more Real-time transmission of the image, video and orientation data to the ground control station Fast data acquisition Can even acquire data in inaccessible locations with no human life risks as in mapping through manned aircraft Since it is a new technology no standard workflow exists and the image processing also varies depending on the quality of the hardware used(Camera quality ,GPS/INS accuracy etc.) Friday, May 23, 2014 4 5. Friday, May 23, 2014 5 Precision farming using UAV data 3D model of an area using UAV Monitoring atmospheric pollution using UAV GROWING USE OF UAV 6. PROBLEMSTATEMENT National Topographical Database has larger contour intervals: 20m for plain region and 40m for mountainous region NO GOOD RESULTS!!! Use of high resolution remote sensing images / aerial photographs RADAR or laser scanning. Require sophisticated work environment with high initial investments Global DEM Sources(ASTER, and SRTM): Lack of verification in context of Nepal Lower spatial resolutions UAV photogrammetry seems to be a promising method for conducting low cost aerial survey In area with smaller spatial extent Undulating topography with inaccessible locations Friday, May 23, 2014 6 7. OBJECTIVES The main objective of the study is to create a Digital Surface Model (DSM) using high resolution images acquired by a digital camera mounted in a UAV platform. The sub-objectives are : To develop working methodology for processing aerial images acquired by using UAV platform. To Georeference UAV images. To be acquainted with different algorithms of image matching Friday, May 23, 2014 7 8. RESOURCESREQUIRED S.N Resources Purpose 1 Data UAV Images To Generate DSM Camera Calibration Parameters Interior Orientation Ground Control Points Exterior orientation, Aerial Triangulation and Accuracy Assessment 2 Software LPS Image Registration and DSM generation ERDAS Imagine Image Registration and DSM generation SAT-PP For image registration and DSM generation NGATE DSM generation Friday, May 23, 2014 8 9. METH0DOLOGY The overall methodology to accomplish this project has been divided into following phases. Phase 1: Data Collection Phase 2: Georeferencing of Images Phase 3: DSM Generation Phase 4: Accuracy assessments Phase 5: Analysis on obtained results Friday, May 23, 2014 9 10. Phase I Data Collection UAV images. GCP Camera Calibration parameters Phase II Georeferencing of Images Interior Orientation Exterior Exterior Orientation/Georeferencing Aerial Triangulation using GCP and tie Points Adjustment of Aerial Triangulation Accuracy assessment of Image Registration Phase III DSM Generation Image matching Mass point generation Interpolation Create DSM Accuracy Assessment Phase IV Analysis Friday, May 23, 2014 10 11. 25 images taken by Sony Nex-5N camera mounted on a AscTec Falcon 8 octocopter Camera calibration parameters for Sony Nex-5N GCP(Ground Control Points) coordinates in global coordinate system(WGS 84) Friday, May 23, 2014 11 DATA COLLECTION 12. IMAGE REGISTRATION Process of relating the image to the exterior ground coordinate system. This phase is further divided into following sub phases. Interior orientation Matching the camera geometry with the image geometry. Camera calibration data can be used to relate the camera setting with the image file. Friday, May 23, 2014 12 13. Exterior orientation Process of establishing the relationship between image coordinate system and ground coordinate system. Based on the principle of collinearity principle, normally a resection in space from object to pixel coordinate Done with the help of Ground Control Points. Friday, May 23, 2014 13 14. Aerial triangulation and its adjustment Process of Measurement of corresponding points in overlapping images Extension (densification) of control points in images Measurement of GCP in images and estimation of orientation parameters of all images (block adjustment). Adjustments of AT: Bundle Adjustment Independent Model Adjustment Strip Adjustment Choice depend upon the nature of the image that we have got. Friday, May 23, 2014 14 15. Accuracy assessment of image registration Carried out through: Maximum and Minimum Errors at X, Y and Z coordinates at Check Points Standard Deviation of X, Y and Z coordinates Mean Standard Deviation Root Mean Square Error Value Friday, May 23, 2014 15 16. DSM GENERATION Through Image Matching - Process of finding matching points in the corresponding stereo image pairs. Different Image Matching Algorithms like Adaptive Algorithm, Hybrid Image Matching Algorithm, Multi-Image Matching Algorithms. Choice for this project depends upon the availability of software version Example: LPS versions 9.2 or higher supports adaptive image algorithm. However, Lower version of LPS supports only area based and feature based algorithm. Mass points generated after image matching are used to obtain the final DSM of the area. Friday, May 23, 2014 16 17. ACCURACY ASSESSMENT OF DSM Accuracy of DSM Obtained by comparing the result obtained from elevation obtained from DSM with the real ground elevation obtained through ground measurement(GCP). Friday, May 23, 2014 17 18. Analysis Analysis on result would include following things Comparison of results of different software and algorithm used on image registration ,image matching and DSM generation Friday, May 23, 2014 18 19. EXPECTEDOUTCOMES Workflow for georeferencing UAV images. Results of different software used for georeferencing UAV images Comparison of different algorithms of image matching for generating DSM. Digital Surface Model of the project site. Friday, May 23, 2014 19 20. REFERENCES Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. (2011). UAV photogrammetry for mapping and 3d modelingcurrent status and future perspectives. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 1. Zongjian, L. (2008). UAV for mappinglow altitude photogrammetric survey. International Archives of Photogrammetry and Remote Sensing, Beijing, China. Ahmad, A., & Samad, A. (2010). Aerial mapping using high resolution digital camera and unmanned aerial vehicle for Geographical Information System. Paper presented at the Signal Processing and Its Applications (CSPA), 2010 6th International Colloquium on. Choi, K., Lee, I., Hong, J., Oh, T. and Shin, S. W. (2009). Developing a UAV -based rapid mapping system for emergency response, In: SPIE, Unmanned Systems Technology XI Orlando, FL, USA. Haarbrink, R., & Eisenbeiss, H. (2008). Accurate DSM production from unmanned helicopter systems. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 37, 1259-1264. Eisenbeiss, H.(2009). UAV photogrammetry: ETH. Grn, A. (2012). From Toys to ToolsUnmanned Aerial Vehicles. GEOinformatics magazine, 15, 14-16. Linder, W. (2009). Digital photogrammetry: Springer. Luhmann, T., Robson, S., Kyle, S., & Harley, I. (2006). Close range photogrammetry: Principles, methods and applications: Whittles. 20 Friday, May 23, 2014 21. Friday, May 23, 2014q 21

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