A Basic Introduction to Digital Image Processing ~~~~~~~~~~ Rev. Ronald J. Wasowski, C.S.C.

1. A Basic Introduction to Digital Image Processing ~~~~~~~~~~ Rev. Ronald J. Wasowski, C.S.C. Associate Professor of Environmental Science University of Portland Portland, Oregon 24 September 2015

2. Basic Digital Image Processing • The structure of digital images • An image processing overview • Image restoration • Image enhancement • Information extraction • Image processing hardware & software

3. The Structure of Digital Images • An array of pixels Picture elements • Rows & columns of pixels • Rows are horizontal • Columns are vertical • Lines & samples of pixels • Lines are horizontal • Samples are vertical • Pixels contain a numerical value • DN Digital number • Lowest value is black 0 • Highest value is white 255

4. An Overview of Image Processing • Three fundamental categories • Image restoration • Images often include defects of various kinds • Image enhancement • Images often need to be made more “readable” • Information extraction • This is always the ultimate goal

5. Image Restoration: Line Drop-outs • The issue • Part or all of some image lines are missing • Scanner or recorder malfunction • Data transmission drop-outs • The solution • Reconstruct the missing data • Use filters to estimate missing pixel values • Linear, bilinear & cubic interpolation algorithms • Some problems • Multiple adjacent image lines are missing • Landsat–7 scan line corrector failure

6. Image Restoration: Banding • The issue • All sensors change over time & at different rates • Multiple sensors in every scanner system • 6 image lines per EW scan for Landsat MSS data • 16 image lines per EW scan for Landsat TM data • 2048 image lines per NS path for pushbroom sensors • The solution • Calculate DN x̅ & s for each scan line set • Force x̅ & s to be equal for entire scan line set • Some problems • Worst just before sensor recalibration • Satellite pushbroomscanners almost impossible • Landsat images rotated to North almost impossible

7. Image Restoration: Line Offsets • The issue • Satellites orbit from N ~11° E to S ~11° W • Constant sunlight illumination azimuth • Satellite’s orbit precesses exactly once per year • Earth rotates from W to E under the satellite • Image acquisition takes 7 to 25 seconds • The solution • Image provider offsets scan lines • Use appropriate software • Some problems • Every satellite scanner system is different • Satellite roll may introduce additional offsets

8. Landsat ETM+ Scan Edge Effects

9. Landsat ETM+ Scan Line Pattern

10. Image Restoration: Random Noise • The issue • Imaging sensor instabilities • Satellite electronic subsystem instabilities • Voltage spikes & dips • Data transmission instabilities • Severe thunderstorms in data transmission path • The solution • Improved subsystems quality • Appropriate filtering of resulting image data • Some problems • Satellites are not designed to be serviceable • Severe degradation makes imagery useless

11. Restoration: Atmospheric Scattering • The issue • Scattering degrades information content • Scattering is selective Rayleigh scattering • Blue light scattered most & reflected infrared light least • The solution • Discard blue spectral band • Scattergrams estimate amount of scattering • Pixels from very dark areas (e.g., water & lava) • Calculate least squares regression line • Subtract intercept DN value from every pixel • Some problems • No dark areas available to calculate intercept • Variable scattering in different image areas

12. Restoration: Geometric Distortions • Relief displacement • High elevations displaced away from center • Low elevations displaced toward center • Imaging platform motions • Roll Wing tips up or down • Pitch Nose tips up or down • Yaw Nose turns into the wind • Imaging system malfunctions • Failure to properly offset scan lines • Landsat–7 scan line corrector failure

13. Relief Displacement Geometry http://www.geog.ucsb.edu/~jeff/115a/lectures/geometry/relief_displacement.jpg

14. Aerial Photo Relief Displacement http://www.fas.org/irp/imint/docs/rst/Sect11/Sect11_4.html

15. Imaging Platform Roll, Pitch & Yaw http://www.flightsim.com/vbfs/content.php?12220-Feature-Around-The-World-2006-Part-5

16. Landsat–7 Scan Line Corrector (SLC) Mount Hood: 25 August 2012

17. Image Enhancement: Contrast • The issue • Entire brightness range seldom used • Distinguish details in both lava fields & glacier ice • Most images appear quite dark & low in contrast • The solution • Spread out DN values over brightness range • Force some pixels to black & others to white • Saturate some number or percent of pixels to 0 & 255 • Default is often 1.00% saturation or 0.39% saturation • Spread out other DN’s using various algorithms • Linear, Gaussian, histogram equalization … • Some problems • Everyone’s visual perception is different

18. Common Contrast Stretches • Linear • DN’s are spread evenly between 0 & 255 • Decisions are made regarding percent saturation • Gaussian • DN’s nearly a bell curve between 0 & 255 • Some flexibility in choosing the value for s • Histogram equalization • DN’s are spread unevenly between 0 & 255 • Cumulative frequency distribution a straight line

19. Image Enhancement: Density Slicing • The issue • The human eye has limited color perception • Human eyes only perceive ~ 1,500 colors • Computer screens have great color capability • Computer screens display ~ 16 million colors • The solution • Drastically reduce number of displayed colors • Some problems • Inaccurate color representation • Inherent limitations of 3-color displays RGB • Sharp Aquos televisions are 4-color displays RGBY

20. Image Enhancement: Edges • The issue • Linear features on images are often subtle • All satellite imagery tends to be low contrast • The solution • Use filters that increase contrast along edges • Directional algorithms • Only enhance lines trending in a particular direction • Selectively accentuate faults zones, joint sets, ridges • Non-directional algorithms • Equally enhance lines trending in all directions • Some problems • Non-linear features may remain low contrast

21. Image Enhancement: Sharpening • The issue • Non-linear images features are often subtle • Tendency of satellite imagery to be low contrast • The solution • Employ filters that increase local contrast • High-pass filters • Low-pass filters • Some problems • Linear features may remain low contrast

22. Image Enhancement: Digital Mosaics • The issue • Entire area not covered by one image • The solution • Obtain enough images to cover entire area • Stitch the images together into a mosaic • Match geometry at edges of images • Match contrast of adjacent images • Match color of adjacent images • Some problems • Lighting differences in different seasons • Land cover differences in different seasons

23. Image Enhancement: Data Merging • The issue • Spatial resolution seldom as good as desired • The solution • Satellites acquire high-resolution pan band • Typically twice as good as multispectral bands • Landsat ETM+ 30 m multispectral & 15 m pan • French SPOT 20 m multispectral & 10 m pan • Use of alternative color spaces • RGB Human eyes sensitive to red, green & blue • IHS Intensity, hue [“color”] & saturation [vividness] • Procedure • Convert 3 appropriate bands from RGB into IHS • Double band size by pixel replication • Replace intensity with high-resolution pan band • Convert from IHS back into RGB

24. Image Enhancement: Synthetic Stereo • The issue • Visual interpretation may benefit from stereo • The solution • Obtain appropriate satellite image • Obtain appropriate DEM • Generate synthetic left & right stereo images • Print & view with traditional stereo viewers • View on-screen with special hardware & software • Some problems • DEM’s may have poor resolution • DEM spacing much larger than image pixel size • Vertical accuracy may be especially bad

25. Information Extraction: PCA • Principal Components Analysis • The problem of spectral autocorrelation • Adjacent bands may contain same information • Visually apparent in scattergrams • DN values of two spectral bands displayed on a graph • Procedure • Generate new set of synthetic spectral bands • Input as many bands as desired • Usually all available spectral bands • Output as many bands as desired • Usually only 3 spectral bands • No more than the number of input spectral bands • Successive PCA images look less like the original scene • Minimize autocorrelation between spectral bands • Specify the percent information content in each PCA band

26. Information Extraction: Ratio Images • The issue • Spectral bands pairs may contain information • Both positive & negative correlations • The solution • Carefully design ratio images • Simple ratios • Normalized ratios • Vegetation index images VI images • NDVI Normalized difference vegetation index • NDVI = (IR1 – Red) / (IR1 + Red) • Some problems • Confusing influence of soil moisture • Specialized VI algorithms

27. Information Extraction: Classification • The issue • Abundant information in multispectral data • The solution • Supervised multispectral classification • The user does know what is in the scene • The user designates areas of each land cover/use type • Training sites • Multispectral color definitions calculated from training sites • Unsupervised multispectral classification • The user does not know what is in the scene • The computer finds colors that are actually there • Multispectral color definitions calculated by sampling pixels • Some problems • Assumption that color correlates with land cover • Fresh asphalt & deep clear water are indistinguishable

28. Information Extraction: Change • The issue • Monitor various kinds of environmental change • The solution • Use multi-date imagery • Raw spectral bands • Classified or transformed images • Calculation of “change vectors” • Similar to statistical trend lines • Some problems • Appropriate imagery in not always available • Mount St. Helens

29. Generic Image Processing Software • Adobe PhotoShop • Import a wide variety of image formats • Limited to BSQ (band sequential) format • Monochrome, RGB color & CMYK color • Wide variety of image enhancements • Contrast, color, sharpness, filters etc. • Export a wide variety of image formats • BMP, GIF, JPG, TIF & many others • Irfanview • Excellent public domain software Windows only

30. Dedicated Image Processing Software • Public domain • MicroMSI Attempt to do things better • Designed as a teaching tool • Works only under Windows • Proprietary • ErdasImagineDe facto world standard • Works under Windows & Unix operating systems • Steep learning curve