poster presentation "generation of high resolution dsm usin uav images"

Download Poster Presentation

Post on 11-Apr-2017

777 views

Category:

Documents

12 download

Embed Size (px)

TRANSCRIPT

  • 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

    earths 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