Automatic Alignment of Vector Data and Orthoimagery for The National - - PowerPoint PPT Presentation

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Automatic Alignment of Vector Data and Orthoimagery for The National - - PowerPoint PPT Presentation

Apr 08, 2024 259 likes •525 views

Automatic Alignment of Vector Data and Orthoimagery for The National Map Craig A. Knoblock, University of Southern Cal Cyrus Shahabi, University of Southern Cal Ching-Chien Chen, Geosemble Technologies E. Lynn Usery, US Geological Survey

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Automatic Alignment of Vector Data and Orthoimagery for The National Map

Craig A. Knoblock, University of Southern Cal Cyrus Shahabi, University of Southern Cal Ching-Chien Chen, Geosemble Technologies

  • E. Lynn Usery, US Geological Survey
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SLIDE 2

Outline

  • Introduction & Motivation

– The National Map

  • Our Approach to align vector and imagery

– Approach overview – Improvements over our previous approach

  • Related Work
  • Conclusion and Future Work
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SLIDE 3

Introduction

  • Geospatial data sources have become widely available
  • Automatically and accurately integrating and aligning two

spatial datasets is a challenging problem Orthoimagery ( in raster format ) Street maps ( in raster format ) Road network ( in vector format )

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SLIDE 4

Motivation: Vector and Imagery Integration

Lat / Long Lat / Long Lat / Long Lat / Long

Challenges

Different projections, accuracy levels, resolutions

result in spatial inconsistencies

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SLIDE 5

Motivation: The National Map

  • The National Map is a government effort to make

geospatial data available for 133 urban areas of the US for Homeland Security

  • Purpose is to make these integrated datasets

available to government organizations to support crisis response and emergency planning, etc.

  • There are no automated techniques for aligning

vector data with orthoimagery and this is a very labor intensive task.

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SLIDE 6

Motivation: The state of the art

  • Traditionally, the problems of vector-imagery and

map-imagery alignment have been in the domain of GIS and Computer Vision

  • In GIS literature

– The alignments were previously performed manually

  • Commercial products: ESEA MapMerger ESRI ArcView; Able

R2V; Intergraph I/RASC

  • In Computer Vision literature

– Alignment was performed automatically based on image processing techniques

  • Often required significant CPU time
  • Accuracy quite poor
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SLIDE 7

The example

  • The Data Sets (for the National Map)

– USGS high resolution color imagery – Road vector data from DOT, MO

Road network USGS 0.3m/p color imagery They are misaligned, and there is no global transformation

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SLIDE 8

The Vector-Imagery conflation approach

Lat / Long Lat / Long Lat / Long Lat / Long Control Point Detection

Intermediate control points

Filtering Technique

Final control points

Triangulation and Rubber-Sheeting

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Finding Control Points Using Localized Template Matching

Road classification Matching by Correlation

road width and road directions road width and road directions

Learned color distribution for road/off road pixels

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Finding Control Points Using Localized Template Matching

Road classification Matching by Correlation

road width and road directions road width and road directions

Learned color distribution for road/off road pixels

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SLIDE 11

Finding Control Points Using Localized Template Matching

Road classification Matching by Correlation

road width and road directions road width and road directions

Learned color distribution for road/off road pixels

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SLIDE 12

Finding Control Points Using Localized Template Matching

Road classification Matching by Correlation

road width and road directions road width and road directions

Learned color distribution for road/off road pixels

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SLIDE 13

Finding Control Points Using Localized Template Matching

Road classification Matching by Correlation

road width and road directions road width and road directions

Learned color distribution for road/off road pixels

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SLIDE 14

Vector median k% control-point vector

X (meter) Y (meter)

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Filtering Control Points Using Vector Median Filter (VMF)

– View the control point pair displacement as vector – Using a fixed ratio (k%) to keep control point pairs that have similar displacement as the median one

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SLIDE 15

Original NAVSTREETS Conflated NAVSTREETS Completeness 44.9 %

74.4 %

Correctness

47.9 % 85 %

Positional Accuracy

Results:NAVSTREETS +

High-res Image

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SLIDE 16

Road Classification Used in Localized Template Matching

  • Before: Bayes classifier based on Hue component of learned road/off-

road pixels

  • Improved: Support Vector Machine (SVM) classifier based on all color

channels (R,G,B) of learned road/off-road pixels

– Much fewer “false positives” and more “true positives”

Original imagery Road-classified pixels based on Bayes classifier Road-classified pixels based on SVM classifier

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Filtering Control Points Using Vector Median Filter (VMF)

  • Improved: Dynamic determine the ratio

– Investigate the cluster around the median vector – Accommodate more control point pairs

X (meter) Y (meter)

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Vector median

Kept control-point vector (clustered vectors around the median vector) Most of the vectors are close to the median vector. This forms a cluster around the median vector

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The Vector-Imagery conflation approach: Triangulation and RubberSheeting

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The Vector-Imagery conflation approach: Triangulation and RubberSheeting

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The Vector-Imagery conflation approach: Triangulation and RubberSheeting

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Improved results: comparing with results based on previous technique

Red lines: Original roads Yellow lines: Conflated results based on previous technique Blue lines: Conflated results based on improved technique

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SLIDE 22

More improved results: comparing with results based on previous technique

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SLIDE 23

Related Work

  • Vector to imagery conflation

– Utilizing matched polygons [Hild et al. 98] – Utilizing matched lines [Filin et al. 00] – Utilizing matched junction-points [Flavie et al. 00] – All above solutions

  • Require lots of CPU time
  • Utilize vector data only for verifying detected features not for

extracting features

– Commercial products: ESRI AreView

  • Pick control points manually
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SLIDE 24

Conclusion

  • Accomplishments

Refinement of pattern recognition procedures for identifying the road intersections in the images Refinement of the filtering procedures for the ground control points Development of methods for matching across image panels Overall improvement of the accuracy of the transformed transportation data to match the images

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SLIDE 25

Future Work

  • Address the alignment of road vector data

with highways

  • Apply the same techniques to

automatically align:

– Vector Parcel Data – Hydrographic Data – Elevation Data