poster presentation "generation of high resolution dsm usin uav images"
Post on 11-Apr-2017
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GENERATION OF HIGH RESOLUTION DSM USING UAV IMAGES
Post Processing of UAV images are better supported by algorithms from Computer
Vision: SIFT Algorithm for feature extraction ,Dense Stereo Matching for Image
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 LMTCKU
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 DSM
LPS-PIX4D LPS-AgiSoft PIX4D-Agisoft
Classical Photogrammetric Workflow Computer Vision Workflow
Photogrammetry UAV Photogrammetry
Data Acquisition Manual/Assisted Assisted/Manual/
Aerial Vehicle Highly stable
Small aerial Vehicles
with certain payload
Image Resolution cm-m mm-m
Ground Coverage Km2 m2-km2
Cameras Well calibrated
Can work with normal
Fudicial Marks Present Absent
Flying Height 100 m-10 km m-km
(not more than 1 km)
No standard workflows
Salient Feature Better control over
the output image
High temporal accuracy
with real time
Digital Surface Models (DSM):
Digital representation of the
earths surface elevation
including natural and artificial
objects like trees or building
Provides a low cost
An emerging field that can
provide very high resolution
datasets for small areas.
Remotely or (semi)
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 Unmanned
Aerial 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 the
DSM is performed through difference of DSM, RMSE and visual interpretation. Although three software: LPS, AgiSoft PhotoScan and PIX4D were used for image processing, the
identified algorithms and limitations in processing are valid for most other commercial photogrammetric software available on the market.
To create a high resolution DSM using images acquired by a digital camera
mounted in a UAV platform.
To orient and georeference UAV images using internal and external orientation
To generate Digital Surface Model.
To compare and analyze the accuracy of DSM generated from different methods
27 high resolution images acquired by a Trimble
UX5 Imaging Rover
(2.4 cm average spatial resolution)
(GCP +Check Points)
Larger 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
For mixed topography, all the algorithms
PIX4D provided the best result in all
Visual Interpretation and Analysis
Elevation (m) Elevation Difference (cm)
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=
RMSE Computation Difference of DSM
DSM generated from LPS DSM generated from PIX4DDSM generated from AgiSoft
Aerial Imagery Camera Parameters
Bundle Block adjustment
UAV-acquired Imagery a. Orientation Parameters
Image by image
2. Point Cloud
Aerial Photogrametry VS UAV Photogrametry