1. Lecture 12: Image Processing
Thursday 11 February 2010
Lecture 12: Image Processing
Reading Ch
Last lecture: Earth-orbiting satellites

2. Image Processing
Because of the way most remote-sensing
texts are organized, what strikes most students
is the vast array of algorithms with odd names
and obscure functions
What is elusive is the underlying simplicity.
Many algorithms are substantially the same –
they have similar purposes
and similar results

3. Image Processing
There are basically five families of
algorithms that do things to images:
Radiometric algorithms
change the DNs
Calibration
Contrast enhancement
2) Geometric algorithms
change the spatial arrangement of pixels or adjust DN’s based on their neighbors’ values
Registration
“Visualization”
Spatial-spectral transformation
Spatial filtering

4. Image Processing
3) Spectral analysis algorithms
are based on the relationship of DNs within a given pixel
Color enhancement
Spectral transformations (e.g., PCA)
Spectral Mixture Analysis
4) Statistical algorithms
characterize or compare groups of radiance data
Estimate geophysical parameters
Spectral similarity (classification, spectral matching)
Input to GIS

5. Image Processing
5) Modeling
calculate non-radiance parameters from the radiance and other data
Estimate geophysical parameters
Make thematic maps
Input to GIS

6. Image Processing
There is a dazzling array of things for the future
professional to become familiar with
I’m trying to over-simplify it to begin with
Most algorithms are handled pretty well in most
remote-sensing texts.
Spectral Mixture Analysis is an exception, so…
- we’ll look at Spectral Mixture Analysis next lecture

7. Image Processing Sequence (single image)
Raw image
data 1.
Image display/inspection 2.
Instrument calibration
Image rectification, cartographic projection, registration, geocoding 3.
Pre-processing 4.
Atmospheric compensation 5.
Pixel illumination-viewing geometry
(topographic compensation)
Working image
data

8. Image Processing Sequence (single image)
Working image
data 6.
Further image processing 7.
Spectral analysis
Selection of training
data/endmembers 8.
Processing
Initial classification or other type
of analysis 9.
Interpretation/verification
or further analysis
10.
Product

10. Band Ratios TITAN B/R G/R B/G Color Ratio Images
Commonly used ratios: - Landsat TM 5/7 for clays, carbonates, vegetation - 3/1 for iron oxide - 2/4 or 3/4 or 5/4 for vegetation
CRC:
R = B/R
G = G/R
B = B/G

11. Ratios
The Vegetation Index (VI) = DN4/DN3 is a ratio. Ratios suppress topographic shading because the cos(i) term appears in both numerator and denominator.

12. NDVI Normalized Difference Vegetation Index
DN4-DN3 is a measure of how much chlorophyll absorption is present, but it is sensitive to cos(i) unless the difference is divided by the sum DN4+DN3.

13. Principal Component Analysis (PCA)
Designed to reduce redundancy in multispectral bands
Topography - shading
Spectral correlation from band to band
Either enhancement prior to visual interpretation or pre-processing for classification or other analysis
Compress all info originally in many bands into fewer bands

14. Principal Component Analysis (PCA)
In the simple case of 45º axis rotation,
The rotation in PCA depends on the data. In the top case, all the image data have similar DN2/DN1 ratios but different intensities, and PC1 passes through the elongated cluster.
In the bottom example, vegetation causes there to be 2 mixing lines (different DN4/DN3 ratios (and the “tasseled cap” distribution such that PC1 still passes through the centroid of the data, but is a different rotation that in the top case.
PC1
PC2

15. Tasseled Cap Transformation
Transforms (rotates) the data so that the majority of the information is contained in 3 bands that are directly related to physical scene characteristics
Brightness (weighted sum of all bands – principal variation in soil reflectance)
Greenness (contrast between NIR and VIS bands
Wetness (canopy and soil moisture)

16. Tasseled Cap Transformation (TCT)
TCT is a fixed rotation that is designed so that the mixing line connecting shadow and sunlit green vegetation parallels one axis and shadow-soil another. It is similar to the PCT.
Soil
Green

17. Next lecture – Spectral Mixture Analysis