Lecture 12: Image Processing

Thursday 12 February

Last lecture: Earth-orbiting satellites

Reading, LKC 7.20-7.21 p. 615 - 621

Image ProcessingImage Processing

Because of the way most remote-sensing texts are organized, what strikes most studentsis 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

Image ProcessingImage Processing

There are basically five families of algorithms that do things to images:

1) Radiometric algorithmschange the DNs

CalibrationContrast enhancement

2) Geometric algorithmschange the spatial arrangement of pixels or adjust DN’s based on their neighbors’ values

Registration“Visualization”Spatial-spectral transformationSpatial filtering

Image ProcessingImage Processing

3) Spectral analysis algorithmsare based on the relationship of DNs within a given pixel

Color enhancementSpectral transformations (e.g., PCA)Spectral Mixture Analysis

4) Statistical algorithmscharacterize or compare groups of radiance data

Estimate geophysical parametersSpectral similarity (classification, spectral matching)Input to GIS

Image ProcessingImage Processing

5) Modelingcalculate non-radiance parameters from the radiance and other data

Estimate geophysical parametersMake thematic mapsInput to GIS

Image ProcessingImage 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

• but see ESS-422 (ESS-590) and Adams & Gillespie, 2006, • “Spectral Remote Sensing of Landscapes.” Cambridge University Press.

Raw imagedata

Instrument calibration

Image rectification, cartographic projection, registration, geocoding

Atmospheric compensation

Pixel illumination-viewing geometry(topographic compensation)

Image display/inspection1.

2.

3.

4.

5.

Pre-processing

Image Processing Sequence(single image)

Working imagedata

Image Processing Sequence(single image)

Working imagedata

Product

Further image processing

Selection of training data/endmembers

Initial classification or other typeof analysis

Interpretation/verificationor further analysis

6.

7.

8.

9.

Processing

Spectral analysis

10.

0

10

20

30

40

50

60

0 1 2 3

Wavelength, micrometers

Ref

lect

ance

, %

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

Band Ratios

TITANTITAN B/R G/R B/G

CRC:R = B/RG = G/RB = B/G

Color Ratio Images

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

Ratios

3

4

3

4

33

443,4

333

444

)cos(

)cos(

)cos(

)cos(

r

r

I

I

irI

irIRATIO

irI

DN

irI

DN

NDVINormalized 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.

3344

3344

3344

3344

333

444

)cos()cos(

)cos()cos(

)cos();cos(

rIrI

rIrINDVI

irIirI

irIirINDVI

irI

DNirI

DN

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

Principal Component Analysis (PCA)

In the simple case of 45º axis rotation,

PC1

PC2

)45cos()45sin(

)45sin()45cos(

432

431

DNDNPC

DNDNPC

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.

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)

Green

Soil

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.

Tasseled Cap Transformation (TCT)

Next lecture – Spectral Mixture Analysis