High resolution satellite imagery for spatial data acquisition

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

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 4 M

14. Result Assessment C.E.: the combination entropy M.G.: the mean gradient W. T.: wavelet transformation C. C.: correlation coefficient Method Image C. E. M.G. C. C. OriginalM1 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

15. 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. 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 Accuracy of road extraction（Unit：%）

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: +852 - 2766 5975 Fax: +852 – 2330 2994 Email LSWZSHI@POLYU.EDU.HK