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Group MembersBiplov BhandariUpendra OliNiroj PantaUttam Pudasaini
Project SupervisorUma Shankar Pandey
Co-SupervisorMr. Nawraj Shrestha
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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???
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Digitizing existing topographical maps Field survey (GPS, Total Station)
Laser Scanning`
RADAR
LIDAR
Cost and Time ineffective
Suited for the area having large spatial extent UAV
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UNMANNED AERIAL VEHICLE (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.)
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Precision farming using UAV data3D model of an area using UAV
Monitoring atmospheric pollution using UAV
GROWING USE OF UAV
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PROBLEM STATEMENT 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
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OBJECTIVESThe 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
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RESOURCES REQUIRED
S.N Resources Purpose1 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
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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
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Phase I
Data Collection
UAV images.
GCP
Camera Calibration parameters
Phase IIGeoreferencing 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
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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)
DATA COLLECTION
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IMAGE REGISTRATIONProcess 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.
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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.
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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.
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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
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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.
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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).
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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
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EXPECTED OUTCOMES 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.
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REFERENCESRemondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. (2011). UAV photogrammetry for mapping and 3d modeling–current status and future perspectives. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 1.
Zongjian, L. (2008). UAV for mapping—low 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.
Grün, A. (2012). From Toys to Tools–Unmanned 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.
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THANK YOU