High resolution satellite imagery for spatial data acquisition

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