Image Classification

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Image Classification
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Image Classification

• Automatically categorize all pixels in an image
  into
• land use/cover classes or themes.
• A process of thematic information extraction
• A process of pattern recognition

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Image Classification

• The process of arranging raw data DNs into
  information classes.
• Two Basic Types
• Supervised
• Unsupervised

Data
Information
Raw Imagery
Extracted Information
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Supervised Classification

• The image analyst supervises the pixel
  categorization process specifying, to the
  computer algorithm, numerical descriptors of the
  various land cover types present in a scene.
• Representative sample sites of known cover type,
  called training areas, are used to compile a
  numerical interpretation key that describes the
  spectral attributes for feature type of interest.

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Unsupervised Classification
In an unsupervised classification, the computer
groups pixels with similar spectral
characteristics into unique clusters according to
some statistically determined criteria. The
analyst then labels and combines the spectral
clusters into information classes.
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Land-use and Land-cover Classification Schemes
Land cover refers to the type of material present
on the landscape (e.g., water, sand, crops,
forest, wetland). Land use refers to what
people do on the land surface (e.g., agriculture,
commerce, settlement).
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Land-use and Land-cover Classification Schemes

• A classification scheme contains taxonomically
  correct definitions of classes of information
  that are organized according to logical criteria.
  
• The classes in the classification system should
  normally be
• mutually exclusive,
• exhaustive, and
• hierarchical.

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Land-use and Land-cover Classification Schemes

• Mutually exclusive means that there is no
  taxonomic overlap of any classes (i.e., deciduous
  forest and evergreen forest are distinct
  classes).
• Exhaustive means that all land-cover classes
  present in the landscape are accounted for and
  none have been omitted.
• Hierarchical means that sublevel classes (e.g.,
  single-family residential, multiple-family
  residential) may be hierarchically combined into
  a higher- level category (e.g., residential) that
  makes sense.

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Land-use and Land-cover Classification Schemes
It is also important for the analyst to realize
that there is a fundamental difference between
information classes and spectral classes.
Information classes are those that human beings
define. Spectral classes are those that are
inherent in the remote sensor data and must be
identified and then labeled by the analyst.
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U.S. Geological Survey Land-Use/Land-Cover
Classification System for Use with Remote Sensor
Data
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Feature Space Scatter plots

• Compares two image bands in feature space
• Basically two histograms displayed on two
  perpendicular axes.

The brighter a particular point is in the
display, the more pixels within the scene having
that unique combination of band values.
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Some Feature Space Concepts
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Pixel Position (X,Y)
255
64, 191
191
191, 127
Band Y
127
64, 64
64
0
255
127
191
64
Band X
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Scatterplot High Correlation
Image with 5 pixels
255
191
Band Y
127
64
0
255
127
191
64
Band X
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Highly Correlated
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Low Correlation
Image with 5 pixels
255
191
Band Y
127
64
0
255
127
191
64
Band X
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Low Correlation
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Well Defined Clusters
Image with 30 pixels and 5 clusters
255
191
Band Y
127
64
0
255
127
191
64
Band X
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Not So Well Defined Clusters
Image with 30 pixels and 5 clusters
255
191
Band Y
127
64
0
255
127
191
64
Band X
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Poorly Defined Clusters Some Class Confusion
255
191
Band Y
127
64
0
255
127
191
64
Band X
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Very Poorly Defined Clusters Total Class
Confusion
255
191
Band Y
127
64
0
255
127
191
64
Band X
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Calculating Cluster Mean
Mean for Red-Dots Cluster
10
7.5
X Mean (2.52.55 510) / 5 5
Band Y
5
Y Mean (2.5 2.5 51010) / 5 6
Cluster Mean 5,6
2.5
0
10
5
7.5
2.5
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Calculating Cluster Variance
Cluster Mean For x 5 Cluster Mean For y 6
Variance(X) for Cluster 7.5 Variance(Y) for
Cluster 8
For Var(x) (2.5 - 5)2 (2.5 - 5)2 (5 - 5)2
(5 - 5)2 (10 - 5)2 /5 (6.25 6.25 0 0
25)/5 37.5 / 5 7.5 For Var(y) (2.5 - 6)2
(2.5 - 6)2 (5 - 6)2 (10 - 6)2 (10 - 6)2
/5 (3.5 3.5 1 16 16/5 40/5 8
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Calculating Cluster Standard Deviation
1 Standard Deviation
10
7.5
Variance(X) for Cluster 7.5 Variance(Y) for
Cluster 8
Band Y
5
2.5
0
10
5
7.5
2.5
Band X
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Calculating Distance in 2d Space
100
75
Band Y
50
25
0
100
50
75
25
Band X
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Parallelepiped View Standard Deviation
255
191
Band Y
127
64
0
255
127
191
64
Band X
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Class Ellipse View Standard Deviation
255
191
Band Y
127
64
0
255
127
191
64
Band X
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Parametric vs. Non-Parametric Signatures

• A parametric signature is based on a statistical
  analysis of the pixels that are in the training
  site or a cluster.
• A parametric decision rule uses statistical
  analysis to assign pixels in an image to a
  particular class. When a given pixel meets the
  parameters of the decision rule for a given class
  the pixel is assigned to that class.
• A non-parametric signature is not based on
  statistics, but on discrete objects within
  feature space.
• With a non-parametric decision rule, if a pixel
  is located within the boundary of the
  non-parametric signature in feature space then
  that pixel will be assigned to the category
  represented by the signature.

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Parametric vs. Non-Parametric Signatures
Parametric Statistically Defined
Non-Parametric User Defined
255
255
TM 4
TM 4
0
0
255
255
TM 3
TM 3
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User Defined Signature (Non-Parametric)
255
191
Band Y
127
64
0
255
127
191
64
Band X
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Supervised Classification

• Supervised A method in which the analyst
  spatially defines training sites representative
  of each desired class (category). The analyst
  then "trains" the computer software to recognize
  varying spectral values in two or more spectral
  bands associated with those training sites. This
  is called signature definition. After signatures
  for each category have been defined, the computer
  then uses those signatures to classify the
  remaining pixels in the study area.

Data
Information
Signature Creation
Training Data Collection
Raw Imagery
Extracted Information
Training Sites
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1. Define Classification Scheme

-Example Land Use/Cover Classification Scheme,
Atlanta Georgia
Metropolitan Area. Classification of Landsat TM
images.
Codes Land use/cover classes Description
1 High-density urban Central business districts, multi-family dwellings, commercial and industrial facilities, high impervious surface areas of institutional facilities, large transportation facilities, e.g. airports, multilane interstate/state highways
2 Low-density urban Single family residential areas, urban recreational areas, cemeteries, playing fields, campus-like institutions, parks, schools, local roads
3 Bare land Areas with sparse vegetation (less than 20), forest clear-cut, fallowed cropland, quarries, strip mines, rock outcrop, sand beach along rivers and lakes
4 Cropland or grassland Row crop agriculture, orchids, vineyards, horticultural businesses, pastures, non-tilted grasses, golf courses
5 Forest Evergreen forest, deciduous forest, and mixed forest
6 Water Rivers, streams, lakes, and reservoirs
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1. Collect Training Data.

• GPS Field Data
• Refer to air photos
• Visually selecting training sites on the original
  Landsat TM images using human intelligence

Cotton Field
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1. Create Training Areas

1. Create training areas for each category.
2. In ERDAS Imagine, we do this by define Area of
   Interest (AOI)

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4. Create Signatures.
A set of statistics that defines the
multi-spectral characteristics of a target
phenomenon or training site.
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5. Choose Best Supervised Algorithm.

• 1. Minimum Distance
• 2. Parallelepiped
• 3. Maximum Likelihood
• with Null class
• without Null class

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Minimum Distance Classifier
255

• Every pixel is assigned to the a category based
  on its distance from cluster means.
• Standard Deviation is not taken into account.
• Disadvantages generally produces poorer
  classification results than maximum likelihood
  classifiers.
• Advantages Useful when a quick examination of a
  classification result is required.

191
127
64
127
191
0
255
64
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Parallelepiped
255

• Pixels inside of the rectangle (defined in
  standard deviations), are assigned the value of
  that class signature.
• Pixels outside of the rectangle (defined by
  standard deviations) are assigned a value of zero
  (NULL).
• Disadvantages poor accuracy, and potential for a
  large number of pixels classified as NULL.
• Advantages A speedy algorithm useful for quick
  results.

191
127
64
127
191
0
255
64
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Maximum Likelihood With Null Class
255

• Pixels inside of a stated threshold (Standard
  Deviation Ellipsoid) are assigned the value of
  that class signature.
• Pixels outside of a stated threshold (Standard
  Deviation Ellipsoid) are assigned a value of zero
  (NULL).
• Disadvantages Much slower than the minimum
  distance or parallelepiped classification
  algorithms. The potential for a large number of
  NULL.
• Advantages more accurate results (depending
  on the quality of ground truth, and whether or
  not the class has a normal distribution).

191
127
64
127
191
0
255
64
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Maximum Likelihood Without Null Class
255

• Pixels inside of a stated threshold (Standard
  Deviation Ellipsoid) are assigned the value of
  that class signature.
• Pixels outside of stated threshold (Standard
  Deviation Ellipsoid) are classified by minimum
  distance rules.
• Disadvantages Slow Algorithm
• Advantages High accuracy with no tied or null
  pixels.

191
127
64
127
191
0
255
64
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Unsupervised Classification

• Unsupervised A method in which the computer
  separates the pixels in an image into classes
  (clusters) with no direction from the analyst.
  After the computer has completed the classifying
  operation the analyst determines the land-cover
  type for each class based on image
  interpretation, ground-truth information, maps,
  field reports.etc. , and assigns each class to a
  specified category (aggregation).

Data
Information
Aggregation
Classification
Raw Imagery (6 bands) 256 Grey Level
Values Each
Classified Image 80 Classes (Clusters)
Extracted Information 11 Categories
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Steps in Unsupervised Classification

1. Define Classification Scheme
2. Configure and Run Classifier
3. Aggregate Classification
4. Label Classes
5. Check Accuracy

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Unsupervised Classification

• Unsupervised classification (commonly referred
  to as clustering) is an effective method of
  partitioning remote sensor image data in
  multispectral feature space and extracting
  land-cover information.
• Compared to supervised classification,
  unsupervised classification normally requires
  only a minimal amount of initial input from the
  analyst. This is because clustering does not
  normally require training data.

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Unsupervised Classification

• Hundreds of clustering algorithms have been
  developed.
• ERDAS Imagine uses clustering algorithm ISODATA
• (Iterative Self-Organizing Data Analysis)

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Classification Based on ISODATA Clustering