automatic digital aerial image resection controlled

AUTOMATIC DIGITAL AERIAL IMAGE RESECTION CONTROLLED BY LIDAR DATA

DELARA, Jr. Roosevelt – UFSM – Federal University of Santa Maria/ Brazil MITISHITA, Edson A. – UFPR - Federal University of the Parana / Brazil VÖ GTLE, Thomas – UK –University of Karlsruhe / Germany BÄ HR, Hans-Peter - UK –University of Karlsruhe / Germany


Introduction

 Summary

 Introduction  Methodology  Experiments  Results  Conclusions and Remarks

 Acknowledgments 3D GeoInfo 2008 Seoul, SK

DELARA, MITISHITA, VÖ TGLE, BÄ HR

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Introduction

 The problem

RGB Image

Intensity Image

3D GeoInfo 2008 Seoul, SK


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Introduction

 Motivation  LIDAR data can be used as control points in photogrammetric tasks [HABIB et al. 2004; DELARA et al. 2004; DALMOLIN et al. 2005; SANTOS, 2005].

 Amateur digital cameras have great potential use in non-conventional Photogrammetry and, after its calibration can be used in several photogrammetric applications [DELARA et al. 2004; HABIB et al. 2004].

 The challenge concerning to automatic exterior orientation of digital aerial images (including the automatic control points extraction) [DELARA, 2007].  Data sets surveyed in distinct epochs. 3D GeoInfo 2008 Seoul, SK


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Introduction

 Objetive  Automatic digital aerial image resection controlled by LIDAR data. ISPRS COMMISSION I IMAGE DATA ACQUISITION - SENSORS AND PLATFORMS ISPRS COMMISSION III PHOTOGRAMMETRIC COMPUTER VISION AND IMAGE ANALYSIS ISPRS COMMISSION V CLOSE-RANGE SENSING: ANALYSIS AND APPLICATIONS



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Methodology STAGE 1 PREPARING IMAGES

Digital Aerial Image - DAI

Imagem de Intensidade - II Rotation – Scale - Cutting

Enhancement Start

STAGE 2

Corner Detection

AUTOMATIC CONTROL POINTS EXTRACTION

Edges Detection Eliminating edges based on a chain dimension Eliminating corners and edges based on the neighborhood Defining new selection criteria

Correspondence between DAI and II corners based on the Cross Correlation Coeficient - CC

Correspondence AT Position Refinement via CC 3D coordinates automatic extraction STAGE 3 DATA INTEGRATION

Eliminating falses correspondences using Spatial Resection rejected Selected Control Points accepted Final Processing (one or more images)



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Enhancement Start

STAGE 2

Corner Detection








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Enhancement Start

STAGE 2

Corner Detection








8





Enhancement Start

STAGE 2

Corner Detection








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Enhancement Start

STAGE 2

Corner Detection








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Methodology

Gray Scale Image

RGB Image

Degree of Artificiality

Natural and Artificial

DoA 

GR GR

GRÜ N(2000),NIEDRÖ ST(2000)

NandA  G  ( R  B) POLIDÓ RIO et al. (2003)

I ' (u, v)  {[ B(u , v)  R(u , v)]  G (u , v)} 3D GeoInfo 2008 Seoul, SK


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Methodology

 Stage 1 - Enhancement

Enhanced Image


Intensity Image


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Enhancement Start

STAGE 2

Corner Detection








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Methodology

 Stage 1- Rotation – Scale - Cutting N

E2

(E0,N0)

E1

N

u

N1

Az (Ec,Nc)

N2

v



E

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Enhancement Start

STAGE 2

Corner Detection








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Enhancement Start

STAGE 2

Corner Detection








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Methodology

 Stage 2 – Corners Detection

Enhanced Image


Intensity Image


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Enhancement Start

STAGE 2

Corner Detection








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Methodology

 Stage 2 – Edges Detection

Enhanced Image


Intensity Image


19





Enhancement Start

STAGE 2

Corner Detection








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Methodology

 Stage 2 – Edges elimination based on chain dimension

Enhanced Image


Intensity Image


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Enhancement Start

STAGE 2

Corner Detection








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Methodology

 Stage 2 – Edges and corners elimination based on neighborhood

Enhanced Image


0

0

0

0

+

0

0

0

0


Intensity Image

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Methodology

 Stage 2 – Edges and corners elimination based on neighborhood

Enhanced Image


Intensity Image


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Enhancement Start

STAGE 2

Corner Detection








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Methodology



Stage 2 -Correspondence between DAI and II corners based on the Cross Correlation Coeficient - CC

Enhanced Image


Intensity Image


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Enhancement Start

STAGE 2

Corner Detection








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Methodology



Stage 2 – Filtering correspondences using Affine Transformation

Enhanced Image


Intensity Image


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Methodology



Stage 2 – Filtering correspondences using Affine Transformation

RGB Image


Intensity Image


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Enhancement Start

STAGE 2

Corner Detection








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Methodology

Enhanced Image


Intensity Image


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Enhancement Start

STAGE 2

Corner Detection








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Methodology

Preliminar systems orientation N Ec

N Nc

Arot P v

du

dv

u

E 3D GeoInfo 2008 Seoul, SK


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Methodology

3D Coordinates extraction N

Planned file Cells

Point Argument Search radius

Raw data file

E 3D GeoInfo 2008 Seoul, SK


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Methodology

 Sample – 3D coordinates extraction

7184500 919.0 918.5 7184499

918.0 917.5

7184498

917.0 916.5 916.0

7184497

915.5 915.0

7184496

914.5 914.0 7184495

677479 677480 677481 677482 677483 677484

LIDAR cloud points and Intensity Image 3D GeoInfo 2008 Seoul, SK


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Enhancement Start

STAGE 2

Corner Detection








36





Enhancement Start

STAGE 2

Corner Detection








37





Enhancement Start

STAGE 2

Corner Detection








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Experiment

Data Sets

LIDAR System Optech ALTM2050 2003/05/09

Sony DSC F717 (5 Mpel) CCD (2560x1920pel²) 2003/06/23

RGB aerial images from Sony DSC F717 LIDAR intensity image (Raw data N,E, h, i) 3D GeoInfo 2008 Seoul, SK


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Experiment

Parameters of interior orientation – Sony DSC F717



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Experiment

Process Criteria



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Results

Methodology application (statistics)

Spatial Resection - MCPE(manual) and ACPE(automatic)



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Results



Image 216

Manual Control

Automatic Control

Discrepancies in SR - Manual (M) Control Points Extraction and Automatic (A) Control Points Extraction



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Conclusions

 The methodology in focus on carrying out automatic integration of digital aerial images and LIDAR data – was efficient, according to the experiment outcomes.  About the enhanced image execution with focus on the correspondence based on the correlation coefficient, a treatment was developed to the whole image, which affords to systematize the acquired correspondences between these kinds of images.

 All stages through the experiment had a continuous process, that is, neither interruption nor human interruption (limited to the process criteria definition), which got the methodology qualification as an automatic one.



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Remarks

 It is recommended the methodology might be applied its use to other situations, for instance, as integrating conventional (digitalized) aerial images and LIDAR data, orbital images and LIDAR data, as using in Close Range Photogrammetry for integrating digital images and terrestrial laser scanning system.  It is suggested improvements and adjustments to be added throughout the developed methodology stages by choosing new algorithms, and by new developing technology.



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AUTOMATIC DIGITAL AERIAL IMAGE RESECTION CONTROLLED BY LIDAR DATA Acknowledgments

 Universidade Federal de Santa Maria (Brazil)

 Universidade Federal do Paraná (Brazil)

 Karlsruhe Universität (Germany)  Conselho Nacional de Desenvolvimento Científico e Tecnoló gico (Brazil)

 Deutscher Akademischer Austauschdienst (Germany)

 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazil) 3D GeoInfo 2008 Seoul, SK


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AUTOMATIC DIGITAL AERIAL IMAGE RESECTION CONTROLLED BY LIDAR DATA The End

Thank you!



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