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High resolution satellite imagery for spatial data
acquisition
Wenzhong (John) ShiThe Hong Kong Polytechnic University
Outline
Image fusion: Multi-band wavelet-based method
Feature extraction: Line segment match method
Geometric correction: Line-based transformation model
High resolution satellite images
An IKONOS image
Several types of available high resolution satellite images
Satellite CompanyLunching
TimeSwath Width
(km)Resolution
( m)
Quick Bird Earth Watch 1999 22 0.6
Ikonos Space imaging 1999 11 1
orbview 3 Orbimmage 1999 8 1
orbview 4 Orbimmage 2000 8 0.5
Eros BWest Indian
Space1999 13.5 1.3
Spot 5 Spot Image 2001 60 5
Technologies for high resolution satellite image processing
Georeferencing Orthorectification Image fusion DEM generation Classification Feature extraction High-resolution aerial
photogrammetry
Our development
Image fusion: Multi-band wavelet-based method
Feature extraction: Line segment match method
Geometric correction: Line-based transformation model
Multi-band wavelet-based image fusion
Two-band and multi-band wavelet transformation
Multi-based wavelet: flexible in scale
The 3-band wavelet transformed image
The 2-band wavelet transformed image
The original image
Image fusion for multi-scale satellite images
Images: panchromatic and multi-spectral images
Spatial resolution Ratio of spatial resolutions:
(a) 2n (n = 1, 2, 3, …), for example 2, 4, 8, etc
(b) 3, 5, 7 etc.
Multi-band wavelet for fusing SPOT panchromatic and multi-spectral image (10m and 30 m)
Multi-band wavelet for fusion of IKONOS Images (1m and 4m)
Two examples
Fusion of IKONOS Images
Four-band wavelet transformation
Test IKONOS Image
1 M 4 M
Result Assessment
Method Image C. E. M.G. C. C. Original M1 10.6102 Images M2 9.7123 5.1062 M3 3.7069 Image fused F1 17.0242 0.9624 by F2 11.7735 10.5206 0.8794 3-band W. T. F3 8.9659 0.9548 Image fused F1 16.7243 0.8798 by F2 11. 2665 9.2284 0.8819 2-band W. T. F3 6.8934 0.7913 Image fused F1 16.4425 0.8241 by F2 11.4623 8.4133 0.7157 IHS method F3 6.0456 0.8098
C.E.: the combination entropyM.G.: the mean gradientW. T.: wavelet transformationC. C.: correlation coefficient
Line Segment Match
method for road extraction
An example of road extraction
A one-meter resolution satellite image of Valparaiso
- Form the foundation of the road network detection method developed -- line segment match method.- A feature-based method for road network extraction from high-resolution satellite image.
- A road with a certain width can be considered as a set of straight-line segments.- To detect a road is to detect the corresponding straight-line segments with a certain length and direction.
The final extracted road network from the image
Filling short small gaps, connecting line segments, deleting crude line segments
Based on the knowledge about the roads
Accuracy of road extraction(Unit: % )
Image Accuracy Omission error
Commission
error Image-1 90.64 9.36
0.82
Image-2 91.02 8.98 0.43
Image-3 90.42 9.58 0.36
Average 90.69 9.31 0.54
The Line Based Transformation Model
Our Research Objectives
To study the applicability and evaluate the accuracy
of the results using existing point-based empirical
mathematical models
To develop a new mathematical model for image
rectification by using line features.
The LBTM developed in this research overcomes most of the
problems encountered when using linear features with the
present generation of rigorous mathematical models.
The model is applicable to various satellite imageries.
The model does not require any further information about the
sensor model and satellite ephemeris data.
It does not need any initial approximation values.
Principle of modeling uncertainties in spatial data and analysis
Further contact:Further contact:
Wenzhong (John) ShiDept. of Land Surveying and GeoinformaticsThe Hong Kong Polytechnic UniversityTel: +852 - 2766 5975Fax: +852 – 2330 2994Email LSWZSHI@POLYU.EDU.HK
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