dsm generation using high resolution uav images

Tuesday, April 11, 2023

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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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Tuesday, April 11, 2023q

THANK YOU