Image Classification: Supervised Methods

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

2. Where in the World?

3. 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

4. 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

5. Supervised vs. Unsupervised Run clustering algorithm Select Training fields Edit/evaluate signatures Identify classes Edit/evaluate signatures Classify image Evaluate classification Evaluate classification

6. NIR Red Supervised vs. Unsupervised 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

7. Training • Training: the process of defining criteria by which spectral patterns are recognized • Spectral signature: result of training that defines a training sample or cluster parametric - 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

8. 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)

9. ERDAS Area of Interest (AOI) tools Seed pixel or region growing dialog

10. Region Growing: good for linear features Spectral Distance = 7 Spectral Distance = 10

11. Region Growing: good for spectrally heterogeneous features Spectral Distance = 5 Spectral Distance = 10

12. 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

13. 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

14. 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

15. 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

16. 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

17. # of pixels 0 255 Digital Number Parametric Assumption: each spectral class exhibits a unimodal normal distribution Bimodal histogram: Mix of Class 1 & 2 Class 1 Class 2

18. Training Aids • Graphical portrayals of training data • histogram (check for normality) “good” “bad”

19. Training Aids • Graphical portrayals of training data • coincident spectral mean plots

20. 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

21. Mix: grass/trees Broadleaf Examine ellipses for gaps and overlaps. Overlapping ellipses ok within information classes; want to limit between info classes Conifer

22. 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

23. 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)

24. 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

25. 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

26. Parallelepiped or Box Classifier 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

27. Parallelepipeds have “corners” Parallelepiped boundary NIR reflectance . Signature ellipse unir Candidate pixel ured Red reflectance Adapted from ERDAS Field Guide

28. NIR reflectance Red reflectance Parallelepiped or Box Classifier: problems Veg 1 Unclassified pixels ?? Veg3 Soil 3 Misclassified pixel Veg 2 Overlap region Soil 2 Soil 1 Water 2 Water 1 Adapted from Lillesand & Kiefer, 1994

29. 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

30. NIR reflectance Red reflectance Minimum Distance to Means Classifier Veg 1 Veg3 Soil 3 Veg 2 Soil 2 Soil 1 Water 2 Water 1 Adapted from Lillesand & Kiefer, 1994

31. Minimum Distance to Means Classifier:Euclidian Spectral Distance Y 92, 153 Distance = 111.2 Yd = 85-153 180, 85 Xd = 180 -92 X

32. 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

33. Feature Space Classifier Analyst draws decision regions in feature space

34. 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

35. # of pixels 0 255 Digital Number Parametric Assumption: each spectral class exhibits a unimodal normal distribution Bimodal histogram: Mix of Class 1 & 2 Class 1 Class 2

36. # of pixels 0 255 Digital Number 2d vs. 1d views of class overlap wj wi Band 2 Band 1 Band 1

37. # of pixels 0 255 Digital Number Probabilities used in likelihood ratio wj wi } p (x | wj) { p (x | wi)

38. Spectral classes as probability surfaces Ellipses defined by class mean and covariance; creates likelihood contours around each spectral class;

39. Sensitive to large covariance values Some classes may have large variance and greatly overlap other spectral classes

40. 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

41. 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

42. 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

43. 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

44. 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

45. # of pixels 0 255 Unclassified Regions Thresholding: define those pixels that have a higher probability of misclassification Class 1 Class 2 Threshold

46. # of pixels 0 Chi Square Thresholding • Chi square distribution used to help define a one-tailed threshold Threshold: values above will remain unclassified

47. 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

48. Hard vs. Fuzzy Classification Hard Classification Forested Wetland Forest Water Fuzzy Classification Adapted from Jensen, 2nd ed. 1996

49. MIR reflectance NIR reflectance Hard vs. Fuzzy Classification Forest Forested Wetland Hard decision boundaries Water Adapted from Jensen, 2nd ed. 1996

50. 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.