Image Preprocessing

Image Preprocessing

Image Preprocessing

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

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

Preprocessing Steps

• Noise reduction/data loss correction

• Atmospheric Correction• Radiometric Calibration• Geometric Correction

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

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)

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)

Preprocessing Steps



Atmospheric Correction

• Reducing the effects of atmospheric conditions on the image values

• 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).


• 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

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


• IMPORTANT : Some atmospheric effects can not be fixed!– Dense clouds– Smoke – Heavy shadows

• Best to exclude these areas from the analysis

Atmospheric Correction – Global Correction Techniques

• Dark object subtraction• Conversion to surface reflectance • Image Normalization

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

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

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

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

Image Normalization

Image 1 brightness value

Imag

e 2

brig

htne

ss v

alue

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0

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100

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Band 1 Band 2 Band 3 Band 4 Band 5 Band 7

1986 image

2000 image

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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

Preprocessing Steps



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

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

Conversion to RadianceRadiance = gain * DN + offset

which is also expressed as:

Radiance = ((LMAX-LMIN)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMIN

QCAL = DN

From Landsat metadata

Lmin and Lmax values used to convert to radiance. Can also use the gain and offset coefficients

Gain and offset values from Landsat metadata file

Conversion to Radiance

Radiance to Reflectance Conversion

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


Solar Zenith Angle

Zenith angle = 90.0 – sun elevation


Preprocessing Steps

• Noise reduction/correcting for data loss


Geometric Correction

• Georeferencing• Image registration• Rectification/

orthorectification

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

http://resources.arcgis.com/en/help/main/10.1/index.html


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

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.

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 NeighborThe pixel value of the output pixel is assigned to the closest input pixel (pixel center, really)

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Resampling Methods

Bilinear InterpolationThe 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 ConvolutionThe 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

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

THE END

Image Preprocessing

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