Remote%20Sensing%20Unsupervised%20Image%20Classification

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Remote SensingUnsupervised Image Classification
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1. Unsupervised Image Classification

• The process requires a minimal amount of initial
  input from the analyst
• A numeric operation searches for natural
  grouping of the spectral properties of pixels
• The analyst determines the information class for
  each spectral class after the spectral classes
  are formed

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1. Unsupervised Classification

• Chain method
• ISODATA
• Spectral mixture analysis
• Object-based image analysis

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2. Chain Method

• Pass 1 builds clusters and calculates their mean
  vectors
• Pass 2 assigns pixels to clusters based on the
  minimum-distance rule

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Pass 1. Cluster Building

• R, a spectral radius used to determine whether a
  new cluster should be formed (e.g., 15
  brightness)
• N, the number of pixels to be evaluated between
  each major merging of the clusters (e.g., 2000)

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Pass 1. Cluster Building

• C, a spectral distance used to determine merging
  clusters when N is reached (e.g., 30 brightness)
• Cmax, the maximum number of spectral clusters
  (categories) (e.g., 20) to be identified

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Pass 1

• The operation evaluates pixels sequentially,
  combining successive pixels into a cluster if
  their spectral distance lt R
• A cluster is complete when N is reached
• If the spectral distance between two clusters is
  lt C, the two clusters are merged, until no
  clusters with distance lt C
• The new mean is the weighted average of the two
  original clusters

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Pass 2. Assigning Pixels

• Assigns pixels based on the minimum distance
  classifier
•  Manual modification based on knowledge of the
  area, co- spectral plots, and interactive display
  

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3. ISODATA Method

• Iterative Self Organizing Data Analysis
  Technique

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ISODATA

• Parameters required
• Cmax, the maximum number of spectral clusters
• T, maximum of pixels whose classes are allowed
  to be unchanged between iterations
• M, the max number of times of classifying pixels
  and calculating cluster mean vectors

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ISODATA

• Minimum members in a cluster (). For example,
  if the lt0.01, the cluster is deleted
• Maximum Std Dev, when a std dev gt specified
  Max-std-dev and the number of members gt 2Min
  members, the cluster is split

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ISODATA

• Split separation when the value is changed from
  0.0, it replaces Std Dev to determine the
  locations of the new mean vectors plus and minus
  this split separation value
• Minimum distance between cluster means. Clusters
  with a weighted distance lt this value (e.g., 3.0)
  are merged

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ISODATA

• It uses a large number of passes
• The initial means are determined based on the
  mean and std dev of each band

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• http//www.youtube.com/watch?vikArEGp-dv0

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Iterations

• Assigns each pixel using the minimum distance
  classifier
• The second to Mth iteration  re-calculate the
  mean vectors  examine Min members()
• Max std dev
• split separation
• Min distance between clusters
• The iteration stops when T or M is reached

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Readings

• Jensen 1996. 2nd Edition or 2005 3rd Edition,
  Introductory Digital Image Processing. Prentice
  Hall.

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4. Classification of Mixed Pixels

• Mixed pixels - when a sensors IFOV covers more
  than one land cover feature
• e.g. tree leaves, grass, and bare soil
• Depends on the spatial resolution of sensors and
  the scale of features
•  Sub-pixel classification
• - spectral mixture analysis

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Spectral Mixture Analysis

• Mixed spectral signatures are compared to pure
  reference spectra
• The pure signature is measured in the lab,
  field, or from images
• Assuming that the variation in an image is a
  mixture of a limited number of features
• Estimates approx proportion of each pure feature
  in a pixel

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Spectral Mixture Analysis ..

• Linear mixture models - assuming a linear
  mixture of pure features
• Endmembers - the pure reference signatures
• Weight - the proportion of the area occupied by
  an endmember
• Output - fraction image for each endmember
  showing the fraction occupied by an
  endmember in a pixel

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Spectral Mixture Analysis ..
Gap, water, Mangrove, forest Kemal Gokkaya 2008
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• Tole L., 2008. Changes in the built vs. non-built
  environment in a rapidly urbanizing region A
  case study of the Greater Toronto Area,
  Computers, Environment and Urban Systems, 32(5)
  355-364.

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Spectral Mixture Analysis ..

• Two basic conditions
• I. The sum of fractions of all endmembers in a
  pixel must equal 1
• ?Fi F1 F2 Fn 1
• II. The DN of a pixel is the sum of the DNs of
  endmembers weighted by their area fractions
• D?? F1 D??1 F2 D??2 Fn D??nE?

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Spectral Mixture Analysis ..

• One D???equation for each band, plus one ?Fi
  equation for all bands
• Number of endmembers number of bands 1
• One exact solution without the E term
• Number of endmembers lt number of bands 1
• Fs and E can be estimated statistically
• Number of endmembers gt number of bands 1
• No unique solution

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Spectral Mixture Analysis ..

• Advantages/characteristics
• - a realistic representation of features
• - a deterministic, not a statistic, method
• - fuzzy set theory vs. fuzzy classification
• Disadvantages
• - does not account for multiple reflections

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5. Object-based Classification

• Also called object-based image analysis (OBIA)
• vs. per pixel classification
• All classifiers so far consider the spectral
  info of a single pixel regardless of its neighbors

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Kutztown GEOEYE-1
Sean Ahearn, Hunter college
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5. Object-based Classification ..

• A two-step process
• I. segmentation of the image into objects
• II. Classiication of the objects
• Works at multiple scales and uses color, shape,
  size, texture, pattern, and context information
  to group pixels into objects

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5. Object-based Classification ..

• Two sets of characteristics can be used to
  classify the objects
• The characteristics of the object itself
• (spectral, texture, shape, etc.)
• The relationship between objects
• (connectivity, proximity, etc.)

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5. Object-based Classification ..

• Advantages
• Disadvantages
• E-cognition, Definiens and Trimble

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Readings

• Chapter 7