1. 1 Image Pre-Processing

2. 2 Digital Image Processing The process of extracting information from digital images obtained from satellites Information regarding each pixel is fed into an algorithm and the result of the computation stored for that pixel Thus for each image being processed by a particular algorithm there is an input and output image Order of processing is important

3. 3 The basic processes Pre-processing- this lecture 1.Image rectification (geometric correction) 2.Radiometric correction (includes noise removal, DN-to-radiance conversion) 3. Atmospheric correction Processing Image enhancement – contrast enhancement and image filtering (may be only visual) Image classification Data merging/data fusion

4. 4 1. Geometric correction Various geometric distortions: Random –Variations in altitude, attitude and velocity of the sensor platform –Atmospheric refraction –Relief displacement –Variable speed of scanning mirror Systematic –Panoramic distortion –Skew distortion due to earth rotation during sweep of IFOV) –Earth curvature – orbit variation due to ellipsoid Output is a geometrically accurate image, registered to a ground coordinate system - georeferenced

5. 5 Systematic distortions Panoramic Distortion –The ground area imaged is proportional to the tangent of the scan angle rather than to the angle itself. Because data are sampled at regular intervals, this produces along-scan distortion. Skew Distortion –Earth rotates as the sensor scans the terrain. This results in a shift of the ground swath being scanned, causing along-scan distortion. –deskewing involves offsetting each scan line successively to west –Skewed parallellogram appearance of images Change in scale at edge of scan (tangential distortion)

6. 6 Correction of distortions 1.Most systematic distortions corrected at ground station 2.Most random distortions are corrected by analysing GCPs in the image to register the image to the ground co- ordinate system (geo-referencing, registering)

7. 7 Geometric correction using ground control points Uses least squares regression Sum of squared difference between image and true coordinates minimised Find four least squared coefficients map x coordinate as function of image c and r map y coordinate as function of image c and r image c coordinate as function of map x and y image r coordinate as function of map x and y Then: x1 = a 0 + a 1 c 1 + a 2 r 1 where a is the coefficient

8. 8 Resampling Process of resampling: which cell values to use? –nearest neighbour –bilinear interpolation ( distance weighted average of 4 nearest pixels)

9. 9 2. Radiometric correction –Need to calibrate data radiometrically due to:- (i) Geometric and atmospheric effects Scene illumination (time of day, season) Viewing geometry Relative position of sensor and illumination (ii) System calibration effects systematic differences in the digital numbers eg. striping conversion of ground radiance to DNs due to differential sensitivity of detectors to different wavebands different sensors convert differently to byte scale Effects of System noise on pixel values

10. 10 Radiometric correction: effects of seasonal change

11. 11 A form of radiometric correction is the conversion of the digital numbers to absolute radiance values DN-to-Radiance conversion eg. for LANDSAT L=((Lmax-Lmin)/QCalmax-QCalmin)*(QCal-QCalmin) + Lmin

12. 12 Noise removal Noise is the unwanted disturbance in an image that is due to limitations in the sensing, digitisation or data recording process The effects of noise range from a degradation to total masking of the true radiometric information content of the digital image

13. 13 Noise removal Critical to the subsequent processing and classification of an image Done to produce an image that is as close to the original radiometry of the scene as possible Noise may either be systematic (banding of multispectral images) to dropped lines or parts of lines

14. 14 The use of moving windows to average out random noise

15. 15 Algorithm for removal of random AND systematic noise

16. 16 Stripe noise Sixteen-line frequency noise in a LANDSAT TM band 2 – Sumatra coastline

17. 17 Image after scan-line noise removal

18. 18 Line drop Dropped line removed by averaging pixels each side of the line

19. 19 3. Atmospheric correction The effects of the atmosphere include reduction in the amount of energy reaching the ground by absorption and scattering increasing the amount of energy reaching the sensor by scattering the radiation (diffuse radiation) decrease in thermal due to w. vapour absorption Atmospheric correction done by empirical methods dark pixel method

20. 20 Dark pixel method: eg. for NIR band

21. 21 SPOT images (SPOTs 1-5) Spot images available is range of pre- processed levels: –1A –1B –2A –2B –Ortho

22. 22 Upper air data for empirical correction Pw represents water vapour http://envf.ust.hk/dataview/profile/current /

23. 23 Level 1A Raw image Detector normalisation for each band Least amount of processing Panoramic effect due to scale change Cost HK$23,300 XS; HK$29,500 Pan Use Radiometric studies, stereoplotting

24. 24 Level 1B Radiometric corrections as for 1A Geometric corrections Panoramic effect Earth rotation and curvature Orbit altitude variation w.r.t. reference ellipsoid Cost: HK$23,300 XS; HK$29,500 Pan Use: Interpretation, thematic studies, stereoplotting

25. 25 Level 2A Corrections Rectified to given projection and rotated to North from satellite data Accuracy 500m planimetric Uses Low accuracy cartographic Cost HK$27,500 XS; HK$33,000 Pan

26. 26 Level 2B Corrections Rectified to control from either maps or survey. Still has relief displacement Accuracy Absolute to 20m RMS Use High accuracy cartographic studies, SPOTView products Cost HK$42,000 both Level 2B with relief correction from DTM Ortho