1. Image Classification

2. Image Classification
The process of sorting pixels into a finite number of individual classes, or categories of data, based on their spectral response (the measured brightness of a pixel across the image bands, as reflected by the pixel’s spectral signature).

3. Spectral Signatures

4. Image Classification
The underlying assumption of image classification is that spectral response of a particular feature (i.e., land-cover class) will be relatively consistent throughout the image.

5. General Approaches to Image Classification
Unsupervised
Supervised

6. Unsupervised Classification
Unsupervised classification (a.k.a., “clustering”) identifies groups of pixels that exhibit a similar spectral response
These spectral classes are then assigned “meaning” by the analyst (e.g., assigned to land-cover categories)

7. Supervised Classification
Supervised classification uses image pixels representing regions of known, homogenous surface composition -- training areas -- to classify unknown pixels.

8. Unsupervised vs. Supervised Classification
Unsupervised: bulk of analyst’s work comes after the classification process
Supervised: bulk of analyst’s work comes before the classification process

9. Advantages and Disadvantages of Unsupervised Classification?
No prior knowledge of the image area is required
Human error is minimized
Unique spectral classes are produced
Relatively fast and easy to perform

10. Disadvantages of Unsupervised Classification
Spectral classes do not represent features on the ground
Does not consider spatial relationships in the data
Can be very time consuming to interpret spectral classes
Spectral properties vary over time, across images

11. Process of Unsupervised Classification
Determine a general classification scheme
Assign pixels to spectral classes (ISODATA)
Assign spectral classes to informational classes

12. Process of Unsupervised Classification
Determine a general classification scheme
Depends upon the purpose of the classification
With unsupervised classification, the scheme does not need to be very specific
Assign pixels to spectral classes (ISODATA)
Assign spectral classes to informational classes

13. Process of Unsupervised Classification
Determine a general classification scheme
Assign pixels to spectral classes (ISODATA)
Group pixels into groups of similar values based on pixel value relationships in multi-dimensional feature space (clustering)
Iterative ISODATA technique is the most common
Assign spectral classes to informational classes

14. Feature Space
Multi-dimensional relationship of the pixel values of multiple image bands across the radiometric range of the image
Allows software to examine the statistical relationship between image bands

15. Feature Space Plot
Feature space images represent two-dimensional plots of pixel values in two image bands (with 8-bit data, in a 255 by 255 feature space)
The greater the frequency of unique pairs of values, the brighter the feature space
Distribution of pixels within the spectral space at bright locations, correspond with important land-cover types
Distribution of pixels within the spectral space at bright locations, correspond with important land-cover types

16. “Iterative Self-Organizing Data Analysis Technique”
ISODATA
“Iterative Self-Organizing Data Analysis Technique”
Uses “spectral distance” between image pixels in feature space to classify pixels into a specified number of unique spectral groups (or “clusters”)
What is spectral distance?

17. ISODATA Parameters & Guidelines
Number of clusters: 10 to 15 per desired land cover class
Convergence threshold: percentage of pixels whose class values should not change between iterations; generally set to 95%
A convergence threshold of 0.95 indicates that processing will cease as soon as 95% or more of the pixels stay the same from one iteration to the next (or 5% or fewer pixels change)
Processing stops when the # of iterations or convergence threshold is reached (whichever comes first)
Should set “reasonable” parameters so that convergence is reached before iterations run out

18. ISODATA Parameters & Guidelines
A convergence threshold of 95% indicates that processing will cease as soon as 95% or more of the pixels stay the same from one iteration to the next (or 5% or fewer pixels change)
Processing stops when the # of iterations or convergence threshold is reached (whichever comes first)
A convergence threshold of 0.95 indicates that processing will cease as soon as 95% or more of the pixels stay the same from one iteration to the next (or 5% or fewer pixels change)
Processing stops when the # of iterations or convergence threshold is reached (whichever comes first)
Should set “reasonable” parameters so that convergence is reached before iterations run out

19. ISODATA Parameters & Guidelines
Maximum number of iterations: ideally, the convergence threshold should be reached
Should set “reasonable” parameters so that convergence is reached before iterations run out
Why is it necessary to set a maximum # of iterations?

20. ISODATA
a) ISODATA initial distribution of five hypothetical mean vectors using +/- 1 standard deviation in both bands as beginning and ending points.
P a) ISODATA initial distribution of five hypothetical mean vectors using +/- 1 standard deviation in both bands as beginning and ending points. B) In the first iteration, each candidate pixel is compared to each cluster mean and assigned to the cluster whose mean is closest in Euclidean distance. c) During the second iteration, a new mean is calculated for each cluster based on the actual spectral locations of the pixels assigned to each cluster, instead of the initial arbitrary calculation. This involves analysis of several parameters to merge or split clusters. After the new cluster mean vectors are selected, every pixel in the scene is assigned to one of the new clusters. D) This split-merge-assign process continues until there is little change in class assignment between iterations (the T threshold is reached) or the maximum number of iterations is reached (M).
this is a simple, 2D illustration
Explain ISODATA iterations; pixels assigned to clusters with closest spectral mean; mean recalculated; pixels reassigned
Continues until maximum iterations or convergence threshold reached

21. ISODATA
b) In the first iteration, each candidate pixel is compared to each cluster mean and assigned to the cluster whose mean is closest
P a) ISODATA initial distribution of five hypothetical mean vectors using +/- 1 standard deviation in both bands as beginning and ending points. B) In the first iteration, each candidate pixel is compared to each cluster mean and assigned to the cluster whose mean is closest in Euclidean distance. c) During the second iteration, a new mean is calculated for each cluster based on the actual spectral locations of the pixels assigned to each cluster, instead of the initial arbitrary calculation. This involves analysis of several parameters to merge or split clusters. After the new cluster mean vectors are selected, every pixel in the scene is assigned to one of the new clusters. D) This split-merge-assign process continues until there is little change in class assignment between iterations (the T threshold is reached) or the maximum number of iterations is reached (M).
this is a simple, 2D illustration
Explain ISODATA iterations; pixels assigned to clusters with closest spectral mean; mean recalculated; pixels reassigned
Continues until maximum iterations or convergence threshold reached

22. ISODATA
c) During the second iteration, a new mean is calculated for each cluster based on the actual spectral locations of the pixels assigned to each cluster.
After the new cluster mean vectors are selected, every pixel in the scene is assigned to one of the new clusters
P a) ISODATA initial distribution of five hypothetical mean vectors using +/- 1 standard deviation in both bands as beginning and ending points. B) In the first iteration, each candidate pixel is compared to each cluster mean and assigned to the cluster whose mean is closest in Euclidean distance. c) During the second iteration, a new mean is calculated for each cluster based on the actual spectral locations of the pixels assigned to each cluster, instead of the initial arbitrary calculation. This involves analysis of several parameters to merge or split clusters. After the new cluster mean vectors are selected, every pixel in the scene is assigned to one of the new clusters. D) This split-merge-assign process continues until there is little change in class assignment between iterations (the T threshold is reached) or the maximum number of iterations is reached (M).
this is a simple, 2D illustration
Explain ISODATA iterations; pixels assigned to clusters with closest spectral mean; mean recalculated; pixels reassigned
Continues until maximum iterations or convergence threshold reached

23. ISODATA
d) This split-merge-assign process continues until there is little change in class assignment between iterations (the threshold is reached) or the maximum number of iterations is reached
P a) ISODATA initial distribution of five hypothetical mean vectors using +/- 1 standard deviation in both bands as beginning and ending points. B) In the first iteration, each candidate pixel is compared to each cluster mean and assigned to the cluster whose mean is closest in Euclidean distance. c) During the second iteration, a new mean is calculated for each cluster based on the actual spectral locations of the pixels assigned to each cluster, instead of the initial arbitrary calculation. This involves analysis of several parameters to merge or split clusters. After the new cluster mean vectors are selected, every pixel in the scene is assigned to one of the new clusters. D) This split-merge-assign process continues until there is little change in class assignment between iterations (the T threshold is reached) or the maximum number of iterations is reached (M).
this is a simple, 2D illustration
Explain ISODATA iterations; pixels assigned to clusters with closest spectral mean; mean recalculated; pixels reassigned
Continues until maximum iterations or convergence threshold reached

24. ISODATA
ISODATA iterations; pixels assigned to clusters with closest spectral mean; mean recalculated; pixels reassigned
Continues until maximum iterations or convergence threshold reached
P a) ISODATA initial distribution of five hypothetical mean vectors using +/- 1 standard deviation in both bands as beginning and ending points. B) In the first iteration, each candidate pixel is compared to each cluster mean and assigned to the cluster whose mean is closest in Euclidean distance. c) During the second iteration, a new mean is calculated for each cluster based on the actual spectral locations of the pixels assigned to each cluster, instead of the initial arbitrary calculation. This involves analysis of several parameters to merge or split clusters. After the new cluster mean vectors are selected, every pixel in the scene is assigned to one of the new clusters. D) This split-merge-assign process continues until there is little change in class assignment between iterations (the T threshold is reached) or the maximum number of iterations is reached (M).
this is a simple, 2D illustration
Explain ISODATA iterations; pixels assigned to clusters with closest spectral mean; mean recalculated; pixels reassigned
Continues until maximum iterations or convergence threshold reached

25. Process of Unsupervised Classification
Determine a general classification scheme
Assign pixels to spectral classes (ISODATA)
Assign spectral classes to informational classes
Once the spectral clusters in the image are identified, the analyst must assign them to the “informational” classes of the classification scheme (i.e., land cover)

26. Spectral to Informational Classes

27. Spectral to Informational Classes
P Grouping (labeling) of the original 20 spectral clusters into information classes. The labeling was performed by analyzing the mean vector locations in bands 3 and 4.

28. Example: Image to be Classified

29. Example: Image to be Classified
Multiple clusters likely represent a single type of “feature” on the ground.
Someone needs to assign a landcover class to all of these clusters; can be difficult and time consuming.

30. General Approaches to Image Classification
Unsupervised
Supervised
Biggest difference between two methods:
Unsupervised -- bulk of work after classification
Supervised -- bulk of work before classification

31. Supervised Classification
Supervised classification uses image pixels representing regions of known, homogenous surface composition -- training areas -- to classify unknown pixels.

32. Supervised Classification
The underlying assumption is that spectral response of a particular feature (i.e., land-cover class) will be relatively consistent throughout the image.
vegetation

33. Advantages
Generates informational classes representing features on the ground
Training areas are reusable (assuming they do not change; e.g. roads)

34. Disadvantages Information classes may not match spectral classes
(e.g., a supervised classification of “forest” may mask the unique spectral properties of pine and oak stands that comprise that forest)
Homogeneity of information classes varies
Difficulty and cost of selecting training sites
Training areas may not encompass unique spectral classes
Unsupervised classification may identify more features (Example: a supervised classification of “forest” may mask the unique spectral properties of pine and oak stands that comprise that forest)

35. Process of Supervised Classification
Determine a classification scheme
Create training areas
Generate training area signatures
Evaluate and refine signatures
Assign pixels to classes using a classifier (a.k.a., “decision rule”)

36. 1 | Determine Classification Scheme
Depends upon the purpose of the classification
Make the scheme as specific as resources and available reference data allow
You can always generalize your classification scheme to make it less specific; making it more specific involves starting over
You can always generalize your classification scheme to make it less specific; making it more specific involves starting over

37. 2 | Create Training Areas
Digitizing: drawing polygons around areas in the image
Seeding: “grows” areas based on spectral similarity to seed pixel
Using existing data: existing maps, field data (GPS, etc.), high-resolution imagery
Feature space image training areas
What are the advantages/disadvantages of each method?
Are these methods mutually exclusive?

38. High degree of control; can incorporate additional imagery
Training Area methods
Method
Advantages
Disadvantages
Digitizing
High degree of control; can incorporate additional imagery
May overestimate class variance; relatively time consuming
Seeding
Auto-assisted; fast
May underestimate class variance
Existing data
Precise map coordinates; represents known ground information
May overestimate class variance; data can be difficult & costly to collect
What is variance? (A measure of the dispersion of a set of data points around their mean value. Mathematically, the average squared deviations from the mean.)

39. Digitizing
Selecting
ROIs
Alfalfa
Cotton
Grass
Fallow

40. Seeding

41. Training Areas “Best Practices”
Number of pixels > 100 per class
Individual training sites should be between 10 to 40 pixels
Sites should be dispersed throughout the image
Uniform and homogeneous sites

42. 3 | Generate Training Areas Signatures
Signatures represent the collective spectral properties of all the training areas defined for a particular class
the most important step in supervised classification

43. Types of Signatures
Parametric: signature that is based on statistical parameters (e.g., mean) of the pixels that are in the training area (normal distribution assumption)
Non-parametric: signature that is not based on statistics, but on discrete objects (polygons or rectangles) in a feature space image
What is a major assumption of parametric signature generation and evaluation? (Normal distribution)

44. Parametric Signatures
e.g., mean of the pixels that are in the training area

45. Parametric Signatures
e.g., mean of the pixels that are in the training area

46. Non-Parametric Signatures
e.g., polygons in a feature space

47. 4 | Evaluate and Refine Signatures
Attempt to reduce or eliminate overlapping, non-homogeneous, non-representative signatures
Signatures should be as “spectrally distinct” as possible

48. Some Signature Evaluation Methods
Ellipse evaluation (feature space)
Contingency matrices
Training area histograms
Signature plots
All evaluation methods are covered in detail in the Field Guide; we’re just going to review a few of the most widely used methods

49. Ellipse Evaluation
What do these ellipses represent? (mean and standard deviation)

50. Contingency Matrix
Contingency analysis produces a matrix showing the percentage of pixels that are classified correctly in a preliminary image classification of only the training areas
It assumes that most of the training area pixels should be assigned to their respective land-cover class
If a significant percentage of training pixels are classified as another land-cover, it indicates that the spectral signatures are not distinct enough to produce an accurate classification of the entire image
It assumes that most of the training area pixels should be assigned to their respective land-cover class.
If a significant percentage of training pixels are classified as another land-cover, it indicates that the spectral signatures are not distinct enough to produce an accurate classification of the entire image

51. Contingency Matrix

52. Training Area Histograms
CAMPBELL FIGURE 11.16

53. Signature Plots

54. Signature Refinement Methods
Refine training area boundaries
Add/delete training areas
Modify classification scheme/merge signatures

55. Merge Signatures

56. Merge Signatures

57. 5 | Assign Pixels to Classes
Each pixel is independently compared to each signature relative to the selected classification criteria, or “decision rule”
Pixels that satisfy the criteria for a class signature are assigned to that class
I’m going to use the terms “classifier” and “decision rule” interchangeably; they mean the same thing

58. Classification “Decision Rules”
Parametric: image is classified based on a statistical representation of the data derived from the training area signatures; all image pixels are classified
Parametric classifiers are “comprehensive”; they assign every pixel in an image to a class (regardless of how well that pixel fits into the classification scheme)
Non-parametric: pixels are classified as objects in feature space; only those pixels within the feature space object are classified
Parametric classifiers are “comprehensive”; they assign every pixel in an image to a class (regardless of how well that pixel fits into the classification scheme)
Both parametric signatures can be used with both parametric and non-parametric classifiers (and vice versa); non-parametric (feature space-derived) signatures can only be used with non-parametric classifiers

59. Non-Parametric “Decision Rules”
Parallelepiped
Feature space

60. Parallelepiped Classifier
The pixels values are compared to upper and lower limits of each signature class (i.e., the min/max pixel values in each band, or the mean of each band +/- 2 standard deviations)

61. Parallelepiped Classifier
leave them unclassified or classify them using a parametric classifier
If the pixel value lies above the low threshold and below the high threshold for all n bands evaluated, it is assigned to that class
When an unknown pixel does not satisfy any of the criteria, it is assigned to an unclassified category
We can visually see the two-dimensional box, but this could be extended to n dimensions.
What happens to the “unclassified” pixels? (You decide -- leave them unclassified or classify them using a parametric classifier)
When might this method be useful? (Show next slide)
P Parallelepiped classifier is digital image classification decision rule based on simple boolean “and/or” logic. If the pixel value lies above the low threshold and below the high threshold for all n bands evaluated, it is assigned to that class. When an unknown pixel does not satisfy any of the boolean logic criteria, it is assigned to an unclassified category. We can visually see the two-dimensional box, but this could be extended to n dimensions or bands.

62. Parallelepiped Classifier
Landsat TM training statistics for five classes measured in bands 4 and 5 displayed as cospectral parallelepipeds.
The upper and lower limit of each parallelepiped is ±1s, superimposed on a feature space plot of bands 4 and 5.
Band 4: confusion between class 1 and 4
Band 5: confusion between class 3 and 4
Both band 4 and 5: separate all 5 classes at ±1s
If only band 4 data were used to classify the scene, there would be confusion between classes 1 and 4, and if only band 5 data were used, there would be confusion between classes 3 and 4. However, when band 4 and 5 data are used at the same time to classify the scene, there appears to be good between-class separability among the five classes (at least at +/- 1s)

63. Parallelepiped Classifier
Advantages: fast; good for non-normal distributions; can limit classification to specific land cover
Disadvantages: classes can include pixels spectrally distant from the signature mean; does not incorporate variability; not all pixels are classified; allows class overlap

64. Feature Space Classifier
Classifies pixels that fall within non-parametric signatures identified in the feature space image not used very often because it is difficult to accurately create and evaluate non-parametric signatures

65. Feature Space Classifier
non-parametric signatures
you decide how they are handled
Same rules apply to unclassified pixels as with the parallelepiped method -- you decide how they are handled

66. Feature Space Classifier
Advantages: good for non-normal distributions and multi-modal signatures (that include many land cover features)
Disadvantages: feature space images are difficult to interpret; allows class overlap

67. Parametric “Decision Rules”
Minimum distance
Maximum likelihood
“Minimum distance” is a relic of earlier times when processing was more difficult; only reason to use either is if your training areas are relatively poor or not normally distributed

68. Minimum Distance Classifier
Classifies pixels based on the spectral distance between the candidate pixel and the mean value of each signature (class) in each image band

69. mean value of each class
Minimum Distance Classifier
mean value of each class

70. Minimum Distance Classifier
The vectors (arrows) represent the distance from candidate pixels a and b to the mean of all classes in a minimum distance to means classification algorithm
Pixel a – Forest
Pixel b - Wetland

71. Minimum Distance Classifier
Advantages: fast; no unclassified pixels
Disadvantages: does not incorporate variability of signatures
In most cases, a maximum likelihood classifier is a better choice

72. Maximum Likelihood Classifier
Classifies pixels based on the probability that a pixel falls within a certain class
If you know that the probabilities are not equal for all classes, you can specify weight factors
For example, if you know that a large percentage of a particular image area is forested, you may want to weight that class with a higher probability than other classes
For example, if you know that a large percentage of a particular image area is forested, you may want to weight that class with a higher probability than other classes
Unless you have very specific knowledge about the types and quantities of land cover in your image, which you usually do not, you should assume equal probabilities for all classes

73. Maximum Likelihood Classifier
Probability of an unknown pixel being one of the classes
If an unknown pixel has brightness values within the wetland region, it has a high probability of being wetland

74. The ellipses represent standard deviations from the mean
Maximum Likelihood Classifier
pixel X would be assigned to forest because the probability is greater for forest than for agriculture.
Minimum distance classifier - Agriculture
The ellipses represent standard deviations from the mean
Which class does pixel “X” most likely bpixel X would be assigned to forest because the probability density of unknown measurement vector X is greater for forest than for agriculture.
elong to? (Forest)
If a minimum distance classifier was being used, which class would it be assigned to? (Agriculture)
The ellipses represent standard deviations from the mean

75. Maximum Likelihood Classifier
Advantages: most accurate; considers variability
Disadvantages: slow; relies heavily on normally distributed signatures

76. Example: Image to be Classified
A visual summary of the supervised classification process

77. Training Data Selection
training areas (not real… just examples)

78. Supervised Classification Results
Note that we end up with the same classes as we do with the unsupervised process; map is different, though

79. Supervised classification
Supervised classification. Identify known a priori through a combination of fieldwork, map analysis, and personal experience as training sites; the spectral characteristics of these sites are used to train the classification algorithm for eventual land- cover mapping of the remainder of the image. Every pixel both within and outside the training sites is then evaluated and assigned to the class of which it has the highest likelihood of being a member.
Unsupervised classification, The computer or algorithm automatically group pixels with similar spectral characteristics (means, standard deviations, covariance matrices, correlation matrices, etc.) into unique clusters according to some statistically determined criteria. The analyst then re-labels and combines the spectral clusters into information classes.

80. Final Thoughts on Supervised Classification
Accuracy vs. Precision
Land cover vs. land use

81. Accuracy & Precision

82. Accuracy & Precision

83. Accuracy & Precision

84. Accuracy & Precision
Relationship between the level of detail required and the spatial resolution of representative remote sensing systems for vegetation inventories.

85. Land Cover vs. Land Use
Land cover refers to the type of material present on the landscape (e.g., water, sand, crops, forest, wetland, human-made materials such as asphalt).
Land use refers to what people do on the land surface (e.g., agriculture, commerce, settlement).
What’s the land COVER of the red pixel? What’s the land USE?
It’s much more difficult to classify land use; object-oriented feature extraction may someday be able to do this

86. Land Cover vs. Land Use The U.S. Geological Survey’s
Land-Use/Land-Cover Classification System for Use with Remote Sensor Data

87. Hard vs. Fuzzy Classification
Supervised and unsupervised classification algorithms typically use hard classification logic to produce a classification map that consists of hard, discrete categories (e.g., forest, agriculture).
Fuzzy classification logic, takes into account the heterogeneous and imprecise nature (mix pixels) of the real world.
Proportion of the m classes within a pixel (e.g., 10% bare soil, 10% shrub, 80% forest). Fuzzy classification schemes are not currently standardized.

89. Pixel-based vs. Object-oriented Classification
Processing the entire scene pixel by pixel. This is commonly referred to as per-pixel (pixel-based) classification.
Object-oriented classification techniques allow the analyst to decompose the scene into many relatively homogenous image objects (referred to as patches or segments) using a multi-resolution image segmentation process
Object-oriented classification based on image segmentation is often used for the analysis of high-spatial-resolution imagery (e.g., 1  1 m Space Imaging IKONOS and 0.61  0.61 m Digital Globe QuickBird)