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Transcript of Image Classification: Supervised Methods Lecture 8 Prepared by R. Lathrop 11//99 Updated 3/06...
![Page 1: Image Classification: Supervised Methods Lecture 8 Prepared by R. Lathrop 11//99 Updated 3/06 Readings: ERDAS Field Guide 5th Ed. Ch 6:234-260.](https://reader036.fdocuments.us/reader036/viewer/2022081721/56649db65503460f94aa838a/html5/thumbnails/1.jpg)
Image Classification: Supervised Methods
Lecture 8
Prepared by R. Lathrop 11//99
Updated 3/06
Readings:
ERDAS Field Guide 5th Ed. Ch 6:234-260
![Page 2: Image Classification: Supervised Methods Lecture 8 Prepared by R. Lathrop 11//99 Updated 3/06 Readings: ERDAS Field Guide 5th Ed. Ch 6:234-260.](https://reader036.fdocuments.us/reader036/viewer/2022081721/56649db65503460f94aa838a/html5/thumbnails/2.jpg)
Where in the World?
![Page 3: Image Classification: Supervised Methods Lecture 8 Prepared by R. Lathrop 11//99 Updated 3/06 Readings: ERDAS Field Guide 5th Ed. Ch 6:234-260.](https://reader036.fdocuments.us/reader036/viewer/2022081721/56649db65503460f94aa838a/html5/thumbnails/3.jpg)
Learning objectives• Remote sensing science concepts
– Basic concept of supervised classification– Major classification algorithms– Hard vs Fuzzy Classification.
• Math Concepts• Skills --Training set selection: Digital polygon vs. seed pixel-
region growing --Training aids: plot of training data, statistical measure
of separability; --Edit/evaluate signatures
-- Applying Classification algorithms
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Supervised vs. Unsupervised Approaches
• Supervised - image analyst "supervises" the selection of spectral classes that represent patterns or land cover features that the analyst can recognize
Prior Decision
• Unsupervised - statistical "clustering" algorithms used to select spectral classes inherent to the data, more computer-automated
Posterior Decision
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Supervised vs. Unsupervised
Edit/evaluate signatures
Select Training fields
Classify image
Evaluate classification
Identify classes
Run clustering algorithm
Evaluate classification
Edit/evaluate signatures
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Supervised vs. Unsupervised
Red
NIR
Supervised Prior Decision: from Information classes in the Image to Spectral Classes in Feature Space
Unsupervised Posterior Decision: from Spectral Classes in Feature Space to Information Classes in the Image
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Training• Training: the process of defining criteria by which
spectral patterns are recognized• Spectral signature: result of training that defines a
training sample or clusterparametric - based on statistical parameters that assume a normal distribution (e.g., mean, covariance matrix)nonparametric - not based on statistics but on discrete objects (polygons) in feature space
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Supervised Training Set Selection
• Objective - selecting a homogenous (unimodal) area for each apparent spectral class
• Digitize polygons - high degree of user control; often results in overestimate of spectral class variability
• Seed pixel - region growing technique to reduce with-in class variability; works by analyst setting threshold of acceptable variance, total # of pixels, adjacency criteria (horiz/vert, diagonal)
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ERDAS Area of Interest (AOI) tools
Seed pixel or region growing dialog
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Region Growing: good for linear features
Spectral Distance = 7 Spectral Distance = 10
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Region Growing: good for spectrally heterogeneous features
Spectral Distance = 5 Spectral Distance = 10
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Supervised Training Set Selection
Whether using the digitized polygon or seed pixel technique, the analyst should select multiple training sites to identify the many possible spectral classes in each information class of interest
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Guided Clustering: hybrid supervised/unsupervised approach
• Polygonal areas of known land cover type are delineated as training sites
• ISODATA unsupervised clustering performed on these training sites
• Clusters evaluated and then combined into a single training set of spectral signatures
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Training Stage
• Training set ---> training vector
• Training vector for each spectral class- represents a sample in n-dimensional measurement space where n = # of bands
for a given spectral class j
Xj = [ X1 ] X1 = mean DN band 1
[ X2] X2 = mean DN band 2
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Classification Training Aids• Goal: evaluate spectral class separability• 1) Graphical plots of training data
- histograms- coincident spectral plots- scatter plots
• 2) Statistical measures of separability - divergence - Mahalanobis distance
• 3) Training Area Classification
• 4) Quick Alarm Classification- paralellipiped
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Parametric vs. Nonparametric Distance Approaches
• Parametric - based on statistical parameters assuming normal distribution of the clusters
e.g., mean, std dev., covariance
• Nonparametric - not based on "normal" statistics, but on discrete objects and simple spectral distance in feature space
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Parametric Assumption: each spectral class exhibits a unimodal normal
distribution
0 255Digital Number
# of pixels
Class 1 Class 2
Bimodal histogram: Mix of Class 1 & 2
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Training Aids
• Graphical portrayals of training data
– histogram (check for normality)
“good”
“bad”
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Training Aids
• Graphical portrayals of training data– coincident spectral
mean plots
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Training Aids
• Scatter plots: each training set sample constitutes an ellipse in feature space
• Provides 3 pieces of information - location of ellipse: mean vector
- shape of ellipse: covariance- orientation of ellipse:
slope & sign of covariance
• Need training vector and covariance matrix
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Red Reflectance
NIRReflectance
Grass
Trees
water
ImperviousSurface &Bare Soil
Spectral Feature Space
Examine ellipses for gaps and overlaps. Overlapping ellipses ok within information classes; want to limit between info classes
Conifer
Broadleaf
Mix: grass/trees
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Training Aids• Are some training sets redundant or overlap too greatly?
•Statistical Measures of Separability: expressions of statistical distance that are sensitive to both mean and variance
- divergence- Mahalanobis distance
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Training Aids
• Training/Test Area classification: look for misclassification between information classes; training areas can be biased, better to use independent test areas
• Quick alarm classification: on-screen evaluation of all pixels that fall within the training decision region (e.g. parallelipiped)
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Classification Decision Process
• Decision Rule: mathematical algorithm that, using data contained in the signature, performs the actual sorting of pixels into discrete classes
• Parametric vs. nonparametric rules
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Parallelepiped or box classifier
• Decision region defined by the rectangular area defined by the highest and lowest DN’s in each band; specify by range (min/max) or std dev.
• Pro: Takes variance into account but lacks sensitivity to covariance (Con)
• Pro: Computationally efficient, useful as first pass• Pro: Nonparametric• Con: Decision regions may overlap; some pixels
may remain unclassified
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Red Reflectance
NIRReflectance
Spectral Feature Space
Upper and lower limit of each box set by either range (min/max) or # of standard devs.
Note overlap in Red but not NIR band
Parallelepiped or Box Classifier
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Parallelepipeds have “corners”
Red reflectance
NIR
reflectance
Adapted from ERDAS Field Guide
.
Parallelepiped boundary
Signature ellipseunir
ured
Candidate pixel
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Parallelepiped or Box Classifier: problems
Red reflectance
NIR
reflectance
Soil 1 Soil 2
Soil 3
Water 1
Water 2
Veg 1
Veg 2
Veg3
Adapted from Lillesand & Kiefer, 1994
Overlap region
Misclassified pixel
??Unclassified pixels
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Minimum distance to means
• Compute mean of each desired class and then classify unknown pixels into class with closest mean using simple euclidean distance
• Con: insensitive to variance & covariance
• Pro: computationally efficient
• Pro: all pixels classified, can use thresholding to eliminate pixels far from means
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Minimum Distance to Means Classifier
Red reflectance
NIR
reflectance
Soil 1 Soil 2
Soil 3
Water 1
Water 2
Veg 1
Veg 2
Veg3
Adapted from Lillesand & Kiefer, 1994
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Minimum Distance to Means Classifier: Euclidian Spectral Distance
X
Y 92, 153
180, 85
Xd = 180 -92
Yd = 85-153Distance = 111.2
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Feature Space Classification
• Image analyst draws in decision regions directly on the feature space image using AOI tools - often useful for a first-pass broad classification
• Pixels that fall within a user-defined feature space class is assigned to that class
• Pro: Good for classes with a non-normal distribution
• Con: Potential problem with overlap and unclassified pixels
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Red Reflectance
NIRReflectance
Spectral Feature Space
Analyst draws decision regions in feature space
Feature Space Classifier
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Statistically-based classifiers
• Defines a probability density (statistical) surface
• Each pixel is evaluated for its statistical probability of belonging in each category, assigned to class with maximum probability
• The probability density function for each spectral class can be completely described by the mean vector and covariance matrix
![Page 35: Image Classification: Supervised Methods Lecture 8 Prepared by R. Lathrop 11//99 Updated 3/06 Readings: ERDAS Field Guide 5th Ed. Ch 6:234-260.](https://reader036.fdocuments.us/reader036/viewer/2022081721/56649db65503460f94aa838a/html5/thumbnails/35.jpg)
Parametric Assumption: each spectral class exhibits a unimodal normal
distribution
0 255Digital Number
# of pixels
Class 1 Class 2
Bimodal histogram: Mix of Class 1 & 2
![Page 36: Image Classification: Supervised Methods Lecture 8 Prepared by R. Lathrop 11//99 Updated 3/06 Readings: ERDAS Field Guide 5th Ed. Ch 6:234-260.](https://reader036.fdocuments.us/reader036/viewer/2022081721/56649db65503460f94aa838a/html5/thumbnails/36.jpg)
2d vs. 1d views of class
overlap
0 255Digital Number
# of pixels
wi
wj
Band 2
Band 1
Band 1
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Probabilities used in likelihood ratio
0 255Digital Number
# of pixels
p (x | wj)p (x | wi)
wi
wj
}{
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Red Reflectance
NIRReflectance
Spectral Feature Space
Ellipses defined by class mean and covariance; creates likelihood contours around each spectral class;
Spectral classes as probability surfaces
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Red Reflectance
NIRReflectance
Spectral Feature Space
Some classes may have large variance and greatly overlap other spectral classes
Sensitive to large covariance values
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Mahalonobis Distance Classifier D = (X-Mc)T (COVc
-1)(X-Mc)
D = Mahalanobis distance c = particular class
X = measurement vector of the candidate pixel
Mc = mean vector of class c COVc = covariance matrix
COVc-1 = inverse of covariance matrix T = transposition
Pro: takes the variability of the classes into account with info from COV matrix
Similar to maximum likelihood but without the weighting factors
Con: parametric, therefore sensitive to large variances
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Maximum likelihood classifier
• Pro: potentially the most accurate classifier as it incorporates the most information (mean vector and COV matrix)
• Con: Parametric procedure that assumes the spectral classes are normally distributed
• Con: sensitive to large values in the covariance matrix
• Con: computationally intensive
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Bayes Optimal approach• Designed to minimize the average (expected) cost
of misclassifying in maximum likelihood approach
• Uses an apriori (previous probability) term to weight decisions - weights more heavily towards common classes
• Example: prior probability suggests that 60 of the pixels are forests, therefore the classifier would more heavily weight towards forest in borderline cases
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Hybrid classification• Can easily mix various classification algorithms in a
multi-step process• First pass: some non-parametric rule (feature space or
paralellipiped) to handle the most obvious cases, those pixels remaining unclassified or in overlap regions fall to second pass
• Second pass: some parametric rule to handle the difficult cases; the training data can be derived from unsupervised or supervised techniques
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Thresholding• Statistically-based classifiers
do poorest near the tails of the training sample data distributions
• Thresholds can be used to define those pixels that have a higher probability of misclassification; these pixels can be excluded and labeled un-classified or retrained using a cluster-busting type of approach
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Thresholding: define those pixels that have a higher probability of
misclassification
0 255Unclassified Regions
# of pixels
Class 1 Class 2 Threshold
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Thresholding• Chi square distribution used to help define a one-
tailed threshold
0Chi Square
# of pixels
Threshold: values above will remain unclassified
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Hard vs. Fuzzy Classification Rules
• Hard - “binary” either/or situation: a pixel belongs to one & only one class
• Fuzzy - soft boundaries, a pixel can have partial membership to more than one class
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Hard vs. Fuzzy Classification
Water Forested Wetland
Forest
Hard Classification
Fuzzy Classification Adapted from Jensen, 2nd ed. 1996
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Hard vs. Fuzzy Classification
NIR reflectance
MIR
reflectance
Water
Forested Wetland
Forest
Adapted from Jensen, 2nd ed. 1996
Hard decision boundaries
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Fuzzy Classification: In ERDAS
•Fuzzy Classification: in the Supervised Classification option, the analyst can use choose Fuzzy Classification and then choose the number of “best classes” per pixel.
•This will create multiple output classification layers, as many as the number of best classes chosen above.
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Fuzzy Classification: In ERDAS•Fuzzy Convolution: calculates the total weighted inverse distance of all the classes in a window of pixels and assigns the center pixel the class with the distance summed over the entire set of fuzzy classification layers. •This has the effect of creating a context-based classification. •Classes with a very small distance value will remain unchanged while classes with higher distance values may change to a neighboring value if there are a sufficient number of neighboring pixels with class values and small corresponding distance values.
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Main points of the lecture
• Training: --Training set selection: Digital polygon vs. seed pixel-region growing --Training aids: plot of training data, statistical measure of separability; --Edit/evaluate signatures. • Classification algorithms:
– box classifier, – minimum distance to means classifier, – feature space classifier, – statistically-based classifiers (maximum likelihood classifier,
Mahalonobis distance classifier)• Hybrid classification: statistical + Threshold method; • Hard vs Fuzzy Classification.
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Homework
1 Homework: Unsupervised classification (Hand up your excel file and figure process);
2 Reading Textbook Ch. 9:337-389;
3 Reading Field Guide Ch. 7:226-231, 235-253.