1. High resolution satellite imagery for spatial data acquisition
Wenzhong (John) Shi
The Hong Kong Polytechnic University

2. Outline Image fusion: Multi-band wavelet-based method
Feature extraction: Line segment match method
Geometric correction: Line-based transformation model

3. High resolution satellite images

4. An IKONOS image

5. Several types of available high resolution satellite images
Company
Lunching Time
Swath Width (km)
Resolution（m)
Quick Bird
Earth Watch
1999 22
0.6
Ikonos
Space imaging 11 1
orbview 3
Orbimmage 8
orbview 4
2000
0.5
Eros B
West Indian Space
13.5
1.3
Spot 5
Spot Image
2001 60 5

6. Technologies for high resolution satellite image processing
Georeferencing
Orthorectification
Image fusion
DEM generation
Classification
Feature extraction
High-resolution aerial photogrammetry

7. Our development Image fusion: Multi-band wavelet-based method
Feature extraction: Line segment match method
Geometric correction: Line-based transformation model

8. Multi-band wavelet-based image fusion

9. Two-band and multi-band wavelet transformation
Multi-based wavelet: flexible in scale
The 2-band wavelet transformed image
The 3-band wavelet transformed image
The original image

10. 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.

11. Two examples
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)

12. Fusion of IKONOS Images
Four-band wavelet transformation

13. Test IKONOS Image
1 M M

14. Result Assessment Original M1 10.6102 Images M2 9.7123 5.1062
C.E.: the combination entropy
M.G.: the mean gradient
W. T.: wavelet transformation
C. C.: correlation coefficient
Result Assessment
Method Image C. E M.G C. C.
Original M
Images M M
Image fused F
by F
3-band W. T F
Image fused F
by F
2-band W. T F
Image fused F
by F
IHS method F

15. method for road extraction
Line Segment Match
method for road extraction

16. An example of road extraction
A one-meter resolution satellite image of Valparaiso

17. - 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.
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.

18. 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

19. 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

20. The Line Based Transformation Model

21. 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.

22. 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.

23. Principle of modeling uncertainties in spatial data and analysis

25. Further contact:
Wenzhong (John) Shi
Dept. of Land Surveying and Geoinformatics
The Hong Kong Polytechnic University
Tel:
Fax: +852 –