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Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

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Page 1: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Vegetation Enhancements (continued)

Lost in Feature Space!

Statistical and Feature Space Transformations

Page 2: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Learning objectives• What is a feature space and how do you

construct one?• Where are plants and soils found in

feature space?• How can we use feature spaces to

enhance vegetation?• What is Principal Component Analysis

(PCA)• What is Kauth’s Tasseled Cap and how is

it different from PCA?

Page 3: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Feature Space Transformations

• Also called “band space”• Difficult to visualize because feature space is n-

dimensional (where n is the number of bands)• Can use feature space to enhance spectral

information using mathematical transformations• Examples are Principal Components Analysis

(PCA), Kauth’s Tasseled Cap, Perpendicular Vegetation Index (PVI), and many more

Page 4: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

What is a feature space??

Red Band

NIR

Band

Green Band

Blue Band

Page 5: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Why is feature space useful?

• A way to visualize pixel data – a different way to see information

• Can transform or analyze a feature space mathematically to isolate groups of pixels that may be related

Page 6: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Creating Feature Space Graphs

• Each axis represents DNs from one satellite band; Multiple axes = multiple bands

• Can plot each pixel in the feature space from an image using its DNs.

Page 7: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Interpreting Feature Space

NIR

Red

Where is vegetation?

Where is soil?

Page 8: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Principal Components Analysis (PCA)

• Transforms the original data (DNs) into new “bands” that isolate important parts of the data (e.g., vegetation).

• Principal component axes (PCs) must be perpendicular to one another

• First 3 PCs usually contain the most useful info• Other PCs are sometimes useful for highlighting

features• PC2 is usually a good vegetation index

Page 9: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Principal Components – 2 bands

Page 10: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Principal Components – 3 bands

Page 11: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Erdas Demo

• Principal Components for Laramie Area

Page 12: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Kauth’s Tasseled Cap

• Like PCA but axes don’t have to be perpendicular to each other

• 1st axis oriented towards overall scene brightness (brightness)

• 2nd axis oriented towards vegetation greeness (greeness)

• 3rd and 4th axes often called “wetness” and “yellowness” – less useful than first two.

Page 13: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Tasseled Cap (cont.)

• Fits all the criteria for a good vegetation index

• Almost as widely used as the NDVI• Excellent index

Page 14: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Tasseled Cap

Page 15: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Creating Your Own Spectral Indices

• Can create custom indices to highlight anything that makes spectra unique

• Can use temporal data just like you use spectral data

• Can build indices for any material, not just vegetation

Page 16: Vegetation Enhancements (continued) Lost in Feature Space! Statistical and Feature Space Transformations

Summary

• Vegetation Indices should highlight the amount of vegetation, the difference between vegetation and soil, and they should reduce atmospheric effects

• Minimize soil background effects if possible• Indices can be customized for particular

applications