as presented hall-beyer srivastava igarss0608 pca texture

8/6/2019 As Presented Hall-Beyer Srivastava IGARSS0608 PCA Texture

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Hall-Beyer & Srivastava IGARSS August 2006 1

Principal Components ofGLCM Texture Measures:

What can they tell us

and are they useful?

Mryka Hall-Beyer and Archana Srivastava




Outline

• Why texture?

• Correlation among the texture measures

• Results of PCA of 8 GLCM textures

– Three window sizes• Practical results

• Conclusions




Why texture?

• Important after spectral reflectance in

identifying and characterising objects

• Different information from spectral data

• Classification: Including a quantitativemeasure of texture should and doesimprove class identification




“Texture Measures”• Grey Level Co-Occurrence Matrix (GLCM)

records – what GL values occur next to what others

– how often they occur

• Calculations based on the GLCM yieldnumbers whose relative value interprets aparticular kind of texture – These are called “measures” from here on

Tutorial: http://fp.ucalgary.ca/mhallbey




Measures used• HOM: homogeneity

• CON: contrast• DIS: dissimilarity

• MEAN: GLCM mean• STD: GLCM standard deviation

• ENT: entropy

• ASM: angular second moment (energy)• COR: GLCM correlation




The practical problem

• There are too many measures

– Can just one work for all image objects?

• If so, which one?

– If not, how many do you need?

• which ones?

• Measures are usually correlated with one

another – Classification needs maximally uncorrelated

data inputs




Often, one or two measures areselected based on

intuitionexperiencesoftware defaults

guessing

There must be a better way!

Haralick in 1973 suggested PCA.




Landsat 5 band 4




Expectations• The texture measure equations lead us to

expect high correlation between: – CON and DIS (positive)

– ENT and DIS (positive)

– HOM and DIS (negative) – ENT and HOM (negative)

– ENT and ASM (negative)

• Expect that these will show up in earlycomponents




Correlation matrix of texture measures25x25 pixel window

1-0.170.100.500.35-0.11-0.050.14COR

-0.171-0.80-0.18-0.11-0.42-0.160.72ASM

0.10-0.8010.46-0.120.790.49-0.94ENT0.50-0.180.4610.020.620.72-0.28STD

0.35-0.11-0.120.021-0.25-0.140.28MEAN

-0.11-0.420.790.62-0.2510.88-0.80DIS

-0.05-0.160.490.72-0.140.881-0.45CON

0.140.72-0.94-0.280.28-0.80-0.451HOM

CORASMENTSTDMEANDISCONHOM

Green: expected positive correlation

Red: expected negative correlation

Blue: high correlation not predicted from calculation method




PC – texture measure loadings25x25 window size

PC1: 50.13% of total vairance

HOMCONDIS

MEANSTD

ENTASM

COR

-1 -0.5 0 0.5 1

C u m u l a t i v e v a r i a n

c e 5 0 . 1

3 %

PC2: 20.85% of total variance

HOMCON

DISMEAN

STDENT

ASMCOR

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 C u m u l a t i v e v a r i a n c e 7 0 . 7

2 %


HOM

CONDIS

MEANSTD

ENTASM

COR

-0.5 0 0.5 1 C u m u l a t i v e v a r i a n c e 8 7 . 6

2 % PC4: 8.53% of total variance

HOM

CONDIS

MEANSTD

ENTASM

COR

-1 -0.5 0 0.5 1 C u m u l a t i v e v a r i a n c e 9 6 . 1

5 %

PC 1 through 4: total 96.15% of dataset variance (PC1-3 87.62%)




PC1: “Connectivity”?• 50.13% of total dataset

variance• Represents contrast of

COR with remaining

measures – Bright pixels: pixels havingboth high COR and low others

• Geographical featureemphasized: linear features

PC1: 50.13% of total vairance

HOMCONDIS

MEANSTD

ENTASM

COR

-1 -0.5 0 0.5 1

Original image

low high

PC1




• 20.85% of total datasetvariance

• Represents Contrast of HOM and MEAN with remaining measures

• Geographical featuresrepresented: land coverdifferences

Original image

low highPC1

PC2: 20.85% of total v ariance

HOMCON

DISMEAN

STDENT

ASMCOR

-0.6 -0.4 -0.2 0 0.2 0.4 0.6

PC2

PC2:

“Interior” textures?




PC3:

Connectivity again?• 16.9% of total dataset

variance

• Mirror image (almost) ofPC1 – Edges have low values

• Represents contrast of COR, HOM and others with ENT and DIS

• Geographical feature

emphasized: linearfeatures.

Original image

low highPC1

PC2


HOMCON

DISMEANSTD

ENTASM

COR

-0.5 0 0.5 1




Summarizing

PCA of these 8 GLCM texture measures

finds two “basic” textures:

• connected/linear features:

– PC1 and 3: 67% of dataset texture variance – COR and HOM in contrast with others

• object “interior” textures

– PC2: 21% of dataset texture variance

– MEAN in contrast with others




Superposition of one connectivity component, one

interior texture component, and original image, 25x25window

r=PC1(edges)

g=originalband 4image

b= PC2(interior)




We tested PCA of these 8 texture measures

for other window sizes on the same image.Similar trends were noted.




5x5 window size

• first 4 PCs each > 10% total variance

• PC1 and 2 connectivity, PC4 interior

• Connectivity components are heavily

loaded with COR and HOM • Interior components are heavily loaded

with MEAN

• “Connectivity” components account for85% of variance, interior components for12%

Rgb=PC1, original, PC4




13x13 window size• First 4 PCs each >10% total variance

• PC1 and 2 connectivity, PC3 and 4 interior• Connectivity components are heavily

loaded with CON

• Interior components are heavily loaded with MEAN and HOM

• “Connectivity” components account for83% of variance, “interior” components12%

Rgb=PC1, original,PC4




Conclusions• In this image, for all tested window sizes there

are two fundamental textures, characterised as“connectivity” and “interior textures”

• “Connectivity” components rely on COR in

combination with other measures , especiallyHOM

• “Interior” textures rely on MEAN in combination

• Connectivity accounts for more texture thaninterior




Further conclusions• Practical: 2 or 3 components capture these two

fundamental textures.• Both textures occur in first 4 PCs but it cannot

be predicted in which.

– Connectivity usually in PC1, interior in 2, or 3, or 4

• COR, HOM and MEAN are important in theircontrast to other measures, it cannot be

concluded that they can be used alone tocapture these two fundamental textures




Unexpected• Against predictions, the expected

correlations (HOM and CON, e.g.) did notcluster in early components.




Remaining question – among many

others

• Does this pattern hold true for very

different scene components? Spatialresolutions?




More informationThis powerpoint, and a more detailed

version with additional data and images,will be posted at

http://fp.ucalgary.ca/mhallbey

as presented hall-beyer srivastava igarss0608 pca texture

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