as presented hall-beyer srivastava igarss0608 pca texture
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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
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Outline
• Why texture?
• Correlation among the texture measures
• Results of PCA of 8 GLCM textures
– Three window sizes• Practical results
• Conclusions
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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
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“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
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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
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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
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Often, one or two measures areselected based on
intuitionexperiencesoftware defaults
guessing
There must be a better way!
Haralick in 1973 suggested PCA.
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Landsat 5 band 4
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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
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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
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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 %
PC3: 16.90% of total variance
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%)
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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
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• 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?
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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
PC3: 16.90% of total variance
HOMCON
DISMEANSTD
ENTASM
COR
-0.5 0 0.5 1
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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
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Superposition of one connectivity component, one
interior texture component, and original image, 25x25window
r=PC1(edges)
g=originalband 4image
b= PC2(interior)
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We tested PCA of these 8 texture measures
for other window sizes on the same image.Similar trends were noted.
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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
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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
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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
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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
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Unexpected• Against predictions, the expected
correlations (HOM and CON, e.g.) did notcluster in early components.
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Remaining question – among many
others
• Does this pattern hold true for very
different scene components? Spatialresolutions?
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More informationThis powerpoint, and a more detailed
version with additional data and images,will be posted at
http://fp.ucalgary.ca/mhallbey