image preprocessing. learning objectives be able to describe when and why image corrections are...
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Image Preprocessing
Image Preprocessing
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Learning Objectives
• Be able to describe when and why image corrections are appropriate or necessary
• Give examples of some common approaches to image correction
• Understand the processing steps of Landsat data
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Image Preprocessing
• Preprocessing is the removal of systematic noise from the data (Rees, 2001). It is the first step in the image processing chain and is usually necessary prior to image classification and analysis.
GOAL : following image preprocessing, all images should appear as if they were acquired from the same sensor
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Preprocessing Steps
• Noise reduction/data loss correction
• Atmospheric Correction• Radiometric Calibration• Geometric Correction
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Preprocessing Steps
• Noise reduction/data loss correction
• Atmospheric Correction• Radiometric Calibration• Geometric Correction
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Noise Reduction
• Two types of noise: global and local
• Global noise = random DN variation at every pixel
- can be reduced using filters (moving windows) or Fourier transform
• Local noise may include errors such as :o Missing scan lineso Image striping
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Missing Scan Lines
• Cause: Sensor timing failure
• Solution: Interpolate to fill in the missing data.
• Missing scan line pixel values are estimated using the values of the pixels in the lines above and below the missing line (based on the principle of spatial autocorrelation)
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Striping
• Caused by an imbalance in detector gains and offsets
• Solution: re-calibrate sensors (adjust pixel DNs from each detector to yield the same mean and standard deviation over the entire image)
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Preprocessing Steps
• Noise reduction/data loss correction
• Atmospheric Correction• Radiometric Calibration• Geometric Correction
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Atmospheric Correction
• Reducing the effects of atmospheric conditions on the image values
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• The value recorded at a given pixel includes not only the reflected radiation from the surface, but the radiation scattered and emitted by the atmosphere as well (path radiance).
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Atmospheric Correction
• Landsat 8 Cirrus band– Used to identify cirrus clouds which may
not be visible with the naked eye– May want to remove those areas when
conducting analyses/research
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Atmospheric Correction - tools
Correction methods for reducing atmospheric effects• Tools for improving both local and global
effects • May improve some areas but cause
artifacts in others (overcorrect)• ERDAS and ENVI both have modules that
are used for atmospheric effects
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Atmospheric Correction
• IMPORTANT : Some atmospheric effects can not be fixed!– Dense clouds– Smoke – Heavy shadows
• Best to exclude these areas from the analysis
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Atmospheric Correction – Global Correction Techniques
• Dark object subtraction• Conversion to surface reflectance • Image Normalization
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Dark Object Subtraction
• Dark objects have little to no reflectance observed by the scanner, so the DN values represent path radiance or the influence of atmospheric effects. By subtracting the value of the DN in each band, you remove that artifact.
Dark object
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Conversion to Surface Reflectance
• Requires knowledge of aerosol conditions at the time of the image acquisition
• Use of radiative transfer models based on optical depths of ozone and particulates in the atmosphere
• Not usually possible for historical analyses
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COST model by Chavez
• Based only on image statistics – does not require field based data of aerosol conditions
• Also utilizes dark object subtraction
ERDAS Model available
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Image to Image Normalization
• Normalize one or more images to a ‘base’ image
• Choose bright, mid and dark image targets that will be consistent in all images
• Extract values and run linear regression
• Use linear equation to adjust the values from the other, non-base images
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Image Normalization
Image 1 brightness value
Imag
e 2
bri
gh
tness v
alu
e
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0
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Band 1 Band 2 Band 3 Band 4 Band 5 Band 7
1986 image
2000 image
0
50
100
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Band 1 Band 2 Band 3 Band 4 Band 5 Band 7
1986 image
2000 image
Comparison of sample 1986/2000 image
bright target values BEFORE
normalization
Comparison of 1986 and 2000 bright
target values AFTER normalization
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Preprocessing Steps
• Noise reduction/data loss correction
• Atmospheric Correction• Radiometric Calibration• Geometric Correction
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Radiometric Calibration
• Reduce inconsistencies across detectors and reduce noise caused by sensor calibration, sun angle, and other conditions
• Useful for comparing across sensors or comparisons across time
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DN (raw value from the sensor)
At-sensor radiance
TOA Reflectance
Surface Reflectance
Calibrate based on gain and offset values
Requires:Earth-sun distanceSolar zenith angleExoatmospheric irradiance
Requires:Knowledge of aerosol propertiesRadiative transfer model
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Conversion to Radiance
Radiance = gain * DN + offset
which is also expressed as:
Radiance = ((LMAX-LMIN)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMIN
QCAL = DN
From Landsat metadata
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Lmin and Lmax values used to convert to radiance. Can also use the gain and offset coefficients
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Gain and offset values from Landsat metadata file
Conversion to Radiance
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Radiance to Reflectance Conversion
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Solar Exoatmospheric Irradiance Values
ETM+ TM
Band watts/(meter squared * µm)
watts/(meter squared * µm)
1 1969.000 1957.00
2 1840.000 1829.00
3 1551.000 1557.00
4 1044.000 1047.00
5 225.700 219.3
7 82.07 74.52
8 1368.000
Radiance to Reflectance Conversion
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Solar Zenith Angle
Zenith angle = 90.0 – sun elevation
Radiance to Reflectance Conversion
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Preprocessing Steps
• Noise reduction/correcting for data loss
• Atmospheric Correction• Radiometric Calibration• Geometric Correction
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Geometric Correction
• Georeferencing• Image registration• Rectification/
orthorectification
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GeoreferencingThe process of assigning map coordinates to image data. The image data are not altered (i.e. DNs do not change assuming NN resampling).
From ArcGIS Resources online
http://resources.arcgis.com/en/help/main/10.1/index.html#//009t000000mn000000
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Image RegistrationThe process of making one image conform geographically to another image. The process may or may not involve rectification. In most cases, some form of geometric transformation will be necessary. This involves ‘warping’ the image using mathematical models.
From ArcGIS Resources online
http://resources.arcgis.com/en/help/main/10.1/index.html#//009t000000mn000000
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Polynomial Transformations
1st-order polynomial equations 2nd-order polynomial equations
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Orthorectification"Orthorectification is the process of removing the effects of image perspective (tilt) and relief (terrain) for the purpose of creating a planimetrically correct image. The resulting orthorectified image has a constant scale wherein features are represented in their 'true' positions. This allows for the accurate direct measurement of distances, angles, and areas."
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Once orthorectified, analyst can extract data from the image. Scale and distance are now true.
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Once orthorectified, analyst can extract data from the image. Scale and distance are now true.
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Steps for Orthorectification
• 1. Use DEM to locate ground control points• 2. Compute mathematical equation
(geometric transformation)• 3. Measure the accuracy of the
transformation• 4. Create a new output image by applying
the mathematical equation to the pixel data and resampling
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Process of converting the original image grid to a new image,
either by projecting to a new coordinate system or altering the
pixel dimensions
Image Resampling
3 common options;1.Nearest neighbor2.Bilinear
interpolation3.Cubic
convolution
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Resampling Methods
Nearest Neighbor
The pixel value of the output
pixel is assigned to the closest
input pixel (pixel center,
really)
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Resampling Methods
Bilinear Interpolation
The pixel value of the output
pixel is based on the weighted
distance to the 4 pixel values
nearest the input pixel
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Resampling Methods
Cubic Convolution
The pixel value of the output
pixel is based on the weighted
distance to the 16 pixel values
(in a 4X4 array) nearest the
input pixel
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Preprocessing Landsat data
Landsat data are currently corrected by USGS/EROS to level 1T which includes the following:• Geometric correction with ground control
points for accurate ground location• Radiometric correction for accurate
measurements at the sensor and no data loss
I. may still want to convert to TOA reflectance for climate change studies
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