GENERATION OF HIGH RESOLUTION DSM USING UAV IMAGES

Introduction

Conclusion

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 LMTCKU

Recommendations

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

Particulars

Aerial

Photogrammetry UAV Photogrammetry

Data Acquisition Manual/Assisted Assisted/Manual/

Automatic

Aerial Vehicle Highly stable

specially designed

aircrafts

Small aerial Vehicles

with certain payload

capacity

GPS/INS

Configurations

cm-dm level

accuracy

cm-10 m

Image Resolution cm-m mm-m

Ground Coverage Km2 m2-km2

Cameras Well calibrated

cameras especially

designed for

photogrammetric

applications

Can work with normal

digital cameras

Fudicial Marks Present Absent

Flying Height 100 m-10 km m-km

(not more than 1 km)

Data Processing

Workflows

Standard

Photogrammetric

Workflow

No standard workflows

Salient Feature Better control over

the output image

quality

High temporal accuracy

with real time

applications

Digital Surface Models (DSM):

Digital representation of the

earth’s surface elevation

including natural and artificial

objects like trees or building

above it.

UAV Photogrammetry:

Provides a low cost

photogrammetric platform.

An emerging field that can

provide very high resolution

datasets 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 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.

Objectives

Main Objective

To create a high resolution DSM using images acquired by a digital camera

mounted in a UAV platform.

Software Used

Sub-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 methods

Data Used

27 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

Parameters

Abstract

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

and vegetation.

For mixed topography, all the algorithms

works fine.

PIX4D provided the best result in all

cases.

.

Visual Interpretation and Analysis

Point

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 DSM

DSM generated from LPS DSM generated from PIX4DDSM generated from AgiSoft

DSM Generation

Aerial Imagery Camera Parameters

Image Matching

Georeferencing

Interior Orientation

Exterior Orientation

Aerial Triangulation

Bundle Block adjustment

Interpolation

DSM Generation

UAV-acquired Imagery a. Orientation Parameters

b. GCPs

1. Initial

Processing

Image Matching

(Between Images)

Automatic Aerial

Triangulation

Bundle Block

adjustment

Image by image

Key point

Extraction

Densified Point

Cloud

2. Point Cloud

Densification

Filtered Point

Cloud

3. DSM

Generation

Aerial Photogrametry VS UAV Photogrametry