1 / 30

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

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. Basic Digital Image Processing. The structure of digital images An image processing overview

sawyer
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related