1. Image Preprocessing
Image Preprocessing

2. 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

3. 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

4. Preprocessing Steps Noise reduction/data loss correction
Atmospheric Correction
Radiometric Calibration
Geometric Correction

5. Preprocessing Steps Noise reduction/data loss correction
Atmospheric Correction
Radiometric Calibration
Geometric Correction

6. 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 :
Missing scan lines
Image striping

7. 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)

8. 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)

9. Preprocessing Steps Noise reduction/data loss correction
Atmospheric Correction
Radiometric Calibration
Geometric Correction

10. Atmospheric Correction
Reducing the effects of atmospheric conditions on the image values

11. 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).

12. 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

13. 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

14. Atmospheric Correction
IMPORTANT : Some atmospheric effects can not be fixed!
Dense clouds
Smoke
Heavy shadows
Best to exclude these areas from the analysis

15. Atmospheric Correction – Global Correction Techniques
Dark object subtraction
Conversion to surface reflectance
Image Normalization

16. 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

17. 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

18. 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

19. 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

20. Image Normalization
Image 2 brightness value
Image 1 brightness value

23. Comparison of 1986 and 2000 bright target values AFTER normalization
Comparison of sample 1986/2000 image bright target values BEFORE normalization
Comparison of 1986 and 2000 bright target values AFTER normalization

24. Preprocessing Steps Noise reduction/data loss correction
Atmospheric Correction
Radiometric Calibration
Geometric Correction

25. 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

26. DN (raw value from the sensor)
Calibrate based on
gain and offset values
At-sensor radiance
Requires:
Earth-sun distance
Solar zenith angle
Exoatmospheric irradiance
TOA Reflectance
Requires:
Knowledge of aerosol properties
Radiative transfer model
Surface Reflectance

27. 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

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

29. Conversion to Radiance
Gain and offset values from
Landsat metadata file

30. Radiance to Reflectance Conversion

31. Solar Exoatmospheric Irradiance Values
Radiance to Reflectance Conversion
ETM+ TM
Band
watts/(meter squared * µm) 1 2 3 4 5
219.3 7
82.07
74.52 8
Solar Exoatmospheric Irradiance Values

32. Radiance to Reflectance Conversion
Solar Zenith Angle
Zenith angle = 90.0 –
sun elevation

33. Preprocessing Steps Noise reduction/correcting for data loss
Atmospheric Correction
Radiometric Calibration
Geometric Correction

34. Geometric Correction Georeferencing Image registration
Rectification/orthorectification

35. Georeferencing
The 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

36. Image Registration
The 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

37. Polynomial Transformations
1st-order polynomial equations
2nd-order polynomial equations
Coefficients (a,b,c,d) are derived from a transformation matrix
x and y are the source coordinates
x’ and y’ are the rectified coordinates
1st and 2nd order polynomials are the most widely used transformation
Images with a high degree of distortion will require a higher-order
transformation

38. 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."

39. Once orthorectified, analyst can extract data from the image
Once orthorectified, analyst can extract data from the image. Scale and distance are now true.

40. Once orthorectified, analyst can extract data from the image
Once orthorectified, analyst can extract data from the image. Scale and distance are now true.

41. 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

42. Image Resampling 3 common options; Nearest neighbor
Bilinear interpolation
Cubic convolution
is resampling only used for image rectification purposes?
Process of converting the original image grid to a new image, either by projecting to a new coordinate system or altering the pixel dimensions

43. Resampling Methods Nearest Neighbor
The pixel value of the output pixel is assigned to the closest input pixel (pixel center, really)
what are a few advantages/disadvantages of each method?

44. 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
what are a few advantages/disadvantages of each method?

45. 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
what are a few advantages/disadvantages of each method?

47. 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
may still want to convert to TOA reflectance for climate change studies

48. Level 1T details
The 1G product available to users from EROS is a radiometrically and geometrically corrected Level 0R image. The correction algorithms employed model the spacecraft and sensor using data generated by onboard computers during imaging events. Primary inputs are the PCD, which includes the attitude and ephemeris profiles, the definitive ephemeris (if available) and the MSCD. Refined parameters from the CPF, ground control points and a digital elevation model are also used to improve the overall geometric fidelity of the standard level-one terrain-corrected (L1T) product.
The L1T correction process utilizes both ground control points (GCP) and digital elevation models (DEM) to attain absolute geodetic accuracy. The WGS84 ellipsoid is employed as the Earth model for the Universal Transverse Mercator (UTM) coordinate transformation. Associated with the UTM projection is a unique set of projection parameters that flow from the USGS General Cartographic Transformation Package. The end result is a geometrically rectified product free from distortions related to the sensor (e.g. jitter, view angle effects), satellite (e.g. attitude deviations from nominal), and Earth (e.g. rotation, curvature, relief).
Geodetic accuracy of the L1T product depends on the accuracy of the GCPs and the resolution of the DEM used*. The 2005 Global Land Survey is used as the source for GCPs while the primary terrain data is the Shuttle Radar Topographic Mission DEM. Scenes that have a quality scores of 99 and less than 40 percent cloud cover are automatically processed, and any archived scene, regardless of cloud cover, can be ordered through one of two EROSweb portals (Product Ordering.)

49. THE END