8. Digital Image Processing


8. Digital Image Processing
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
• Image
• Image
• Information
• Image processing hardware & software
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
• Highest value is white
An Overview of Image Processing
• Three fundamental categories
– Image
• Images often include defects of various kinds
– Image
• Images often need to be made more “readable”
– Information extraction
• This is always the ultimate goal
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
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 pushbroom scanners
almost impossible
– Landsat images rotated to North almost impossible
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
Landsat ETM+ Scan Edge Effects
Landsat ETM+ Scan Line Pattern
Image Restoration: Random Noise
• The issue
– Imaging
– Satellite
electronic subsystem
• Voltage spikes & dips
– Data
• 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
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
Restoration: Geometric Distortions
• Relief displacement
– High elevations displaced away from center
– Low elevations displaced toward center
• Imaging platform motions
– Roll
– Pitch
– Yaw
Wing tips
up or down
Nose tips
up or down
Nose turns into the wind
• Imaging system malfunctions
– Failure to properly offset scan lines
– Landsat–7 scan line corrector failure
Relief Displacement Geometry
Aerial Photo Relief Displacement
Imaging Platform Roll, Pitch & Yaw
Landsat–7 Scan Line Corrector (SLC)
Mount Hood: 25 August 2012
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
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
Image Enhancement: Density Slicing
• The issue
– The human eye has limited color perception
• Human eyes only perceive ~ 1,500
– 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
• Sharp Aquos televisions are 4-color displays
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
– 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
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
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
of adjacent images
• Some problems
– Lighting
differences in different seasons
– Land cover differences in different seasons
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
Human eyes sensitive to red, green & blue
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
Image Enhancement: Synthetic Stereo
• The issue
– Visual interpretation may benefit from stereo
• The solution
– Obtain appropriate satellite image
– Obtain appropriate
– 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
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
Information Extraction: Ratio Images
• The issue
– Spectral bands pairs may contain information
• Both positive & negative correlations
• The solution
– Carefully design ratio images
• Simple
• Normalized ratios
– Vegetation index images
VI images
Normalized difference vegetation index
– NDVI = (IR1 – Red) / (IR1 + Red)
• Some problems
– Confusing influence of soil moisture
• Specialized VI algorithms
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
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
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
Dedicated Image Processing Software
• Public domain
– MicroMSI
Attempt to do things better
• Designed as a teaching tool
• Works only under Windows
• Proprietary
– Erdas Imagine
De facto world standard
• Works under Windows & Unix operating systems
• Steep learning curve

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