High resolution satellite imagery for spatial data acquisition Wenzhong (John) Shi The 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 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

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 2-band wavelet transformed image The 3-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.

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)

Fusion of IKONOS Images Four-band wavelet transformation

Test IKONOS Image 1 M 4 M

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 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

method for road extraction Line Segment Match method for road extraction

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

- 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.

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: 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