Pixel-based image classification

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Pixel-based image classification

• Lecture 8

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What is image classification or pattern
recognition

• Is a process of classifying multispectral
  (hyperspectral) images into patterns of varying
  gray or assigned colors that represent either
• clusters of statistically different sets of
  multiband data, some of which can be correlated
  with separable classes/features/materials. This
  is the result of Unsupervised Classification, or
• numerical discriminators composed of these sets
  of data that have been grouped and specified by
  associating each with a particular class, etc.
  whose identity is known independently and which
  has representative areas (training sites) within
  the image where that class is located. This is
  the result of Supervised Classification.
• Spectral classes are those that are inherent in
  the remote sensor data and must be identified and
  then labeled by the analyst.
• Information classes are those that human beings
  define.

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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.
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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).
• Conversely, it is also possible to use fuzzy set
  classification logic, which 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.

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Pixel-based vs. Object-oriented classification

• In the past, most digital image classification
  was based on 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. The various
  statistical characteristics of these homogeneous
  image objects in the scene are then subjected to
  traditional statistical or fuzzy logic
  classification. 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).

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Knowledge-based information extraction
Artificial Intelligence

• Neural network
• Decision tree
• Support vector machine (SVM)

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Purposes of classification

• Land use and land cover (LULC)
• Vegetation types
• Geologic terrains
• Mineral exploration
• Alteration mapping
• .

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Example spectral plot

• Two bands of data.
• Each pixel marks a location in this 2d spectral
  space
• Our eyes can split the data into clusters.
• Some points do not fit clusters.

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1. Unsupervised classification

• Uses statistical techniques to group
  n-dimensional data into their natural spectral
  clusters, and uses the iterative procedures
• label certain clusters as specific information
  classes
• K-mean and ISODATA
• For the first iteration arbitrary starting values
  (i.e., the cluster properties) have to be
  selected. These initial values can influence the
  outcome of the classification.
• In general, both methods assign first arbitrary
  initial cluster values. The second step
  classifies each pixel to the closest cluster. In
  the third step the new cluster mean vectors are
  calculated based on all the pixels in one
  cluster. The second and third steps are repeated
  until the "change" between the iteration is
  small. The "change" can be defined in several
  different ways, either by measuring the distances
  of the mean cluster vector have changed from one
  iteration to another or by the percentage of
  pixels that have changed between iterations.
• The ISODATA algorithm has some further
  refinements by splitting and merging of clusters.
  
• Clusters are merged if either the number of
  members (pixel) in a cluster is less than a
  certain threshold or if the centers of two
  clusters are closer than a certain threshold.
• Clusters are split into two different clusters if
  the cluster standard deviation exceeds a
  predefined value and the number of members
  (pixels) is twice the threshold for the minimum
  number of members.

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• Advantages
• Requires no prior knowledge of the region
• Human error is minimized
• Unique classes are recognized as distinct units
• Disadvantages
• Classes do not necessarily match informational
  categories of interest
• Limited control of classes and identities
• Spectral properties of classes can change with
  time

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• Distance Measures are used to group or cluster
  brightness values together
• Euclidean distance between points in space is a
  common way to calculate closeness

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K-means (unsupervised)

1. A set number of cluster centers are positioned
   randomly through the spectral space.
2. Pixels are assigned to their nearest cluster.
3. The mean location is re-calculated for each
   cluster.
4. Repeat 2 and 3 until movement of cluster centres
   is below threshold.
5. Assign class types to spectral clusters.

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Example k-means
1. First iteration. The cluster centers are set
at random. Pixels will be assigned to the nearest
center.
2. Second iteration. The centers move to the
mean-center of all pixels in this cluster.
3. N-th iteration. The centers have stabilized.
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Example ISODATA
1. Data is clustered but blue cluster is very
stretched in band 1.
2.Cyan and green clusters only have 2 or less
pixels. So they will be removed.
3. Either assign outliers to nearest cluster, or
mark as unclassified.
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ISODATA Initial Cluster Values (properties)

• number of classes
• maximum iterations
• pixel change threshold (0 - 100) (The change
  threshold is used to end the iterative process
  when the number of pixels in each class changes
  by less than the threshold. The classification
  will end when either this threshold is met or the
  maximum number of iterations has been reached)
• initializing from statistics (Erdas) or from
  input (ENVI) (the initial values to put in for
  ENVI are minimum pixel in class, maximum class
  stdv, minimum class distance, maximum merge
  pairs)

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Maximum Class Stdv (in pixel value). If the stdv
of a class is larger than this threshold then the
class is split into two classes. Minimum class
distance (in pixel value) between class means. If
the distance between two class means is less than
the minimum value entered, then ENVI merges the
classes. Optional Maximum stdev from mean (1 to
3s) and maximum distance error (in pixel value).
If any of these two setup, the some pixels might
not be classified.
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5-10 classes, 8 iterations, 5 for change
threshold, (MinP 5, MaxSD 1, MinD 5, MMP 2)
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1-5 classes, 11 iterations, 5 for change
threshold, (MinP 5, MaxSD 1, MinD 5, MMP 2)
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5 classes
10 classes
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2. Supervised classificationtraining sites
selection

• Based on known a priori through a combination of
  fieldwork, map analysis, and personal experience
• on-screen selection of polygonal training data
  (ROI), and/or
• on-screen seeding of training data (ENVI does
  not have this, Erdas Imagine does).
• The seed program begins at a single x, y location
  and evaluates neighboring pixel values in all
  bands of interest. Using criteria specified by
  the analyst, the seed algorithm expands outward
  like an amoeba as long as it finds pixels with
  spectral characteristics similar to the original
  seed pixel. This is a very effective way of
  collecting homogeneous training information.
• From spectral library of field measurements

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• Advantages
• Analyst has control over the selected classes
  tailored to the purpose
• Has specific classes of known identity
• Does not have to match spectral categories on the
  final map with informational categories of
  interest
• Can detect serious errors in classification if
  training areas are missclassified

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• Disadvantages
• Analyst imposes a classification (may not be
  natural)
• Training data are usually tied to informational
  categories and not spectral properties
• Remember diversity
• Training data selected may not be representative
• Selection of training data may be time consuming
  and expensive
• May not be able to recognize special or unique
  categories because they are not known or small

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Statistic extraction of each training site
Each pixel in each training site associated with
a particular class (c) is represented by a
measurement vector, Xc Average of all pixels in
a training site called mean vector, Mc a
covariance matrix of Vc.
where BVi,j,k is the brightness value for the
i,jth pixel in band k. µck represents the mean
value of all pixels obtained for class c in band
k. Covckl is the covariance of class c between
bands l through k.
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Selecting ROIs
Alfalfa
Cotton
Grass
Fallow
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Spectra of ROIs from ETM image
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Spectra from library
Resampled to match TM/ETM, 6 bands
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Supervised classification methods

• Various supervised classification algorithms may
  be used to assign an unknown pixel to one of m
  possible classes. The choice of a particular
  classifier or decision rule depends on the nature
  of the input data and the desired output.
  Parametric classification algorithms assumes that
  the observed measurement vectors Xc obtained for
  each class in each spectral band during the
  training phase of the supervised classification
  are Gaussian that is, they are normally
  distributed. Nonparametric classification
  algorithms make no such assumption.
• Several widely adopted nonparametric
  classification algorithms include
• one-dimensional density slicing
• parallepiped,
• minimum distance,
• nearest-neighbor, and
• neural network and expert system analysis.
• The most widely adopted parametric classification
  algorithms is the
• maximum likelihood.
• Hyperspectral classification methods
• Binary Encoding
• Spectral Angle Mapper
• Matched Filtering
• Spectral Feature Fitting
• Linear Spectral Unmixing

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2.1 Parallepiped

• This is a widely used digital image
  classification decision rule based on simple
  Boolean and/or logic.

If a pixel value lies above the low threshold and
below the high threshold for all n bands being
classified, it is assigned to that class. If the
pixel value falls in multiple classes, ENVI
assigns the pixel to the last class matched.
Areas that do not fall within any of the
parallelepipeds are designated as unclassified.
In ENVI, you can use 1-3?
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This method is a computationally efficient
method. But an unknown pixel might meet the
criteria of more than one class and is always
assigned to the first class for which it meets
all criteria. The Minimum Distance to Means can
assign any pixel to just one class.
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Parallelepiped example
Training classes plotted in spectral space. In
this example using 2 bands.
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Parallelepiped example continued

• Each class type defines a spectral box
• Note that some boxes overlap even though the
  classes are spatially separable.
• This is due to band correlation in some classes.
• Can be overcome by customising boxes.

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1 means 1 stdev from mean, 2 means 2 stdev from
mean, 3 means 3 stdev from mean Use 1, you will
classify the closest pixels to the class Use 3,
you will include some not so closest pixels to
the class
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2.2 Minimum distance
The distance used in a minimum distance to means
classification algorithm can take two forms the
Euclidean distance based on the Pythagorean
theorem and the round the block distance. The
Euclidean distance is more computationally
intensive, but it is more frequently used
All pixels are classified to the nearest class
unless a standard deviation or distance threshold
is specified, in which case some pixels may be
unclassified if they do not meet the selected
criteria.
e.g. the distance of point a to class forest is
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If either Max stdev or Max distance error is not
set, all pixels will be classified. If the Max
stdev from mean is set at 2 (stdev), then the
pixels with values outside the mean 2s will not
be classified. If the Max distance error is set
at 4.2 (pixel value), then the pixels with
distance larger than 4.2 will not be classified.
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2.3 Maximum likelihood

• Instead based on training class multispectral
  distance measurements, the maximum likelihood
  decision rule is based on probability.
• The maximum likelihood procedure assumes that
  each training class in each band are normally
  distributed (Gaussian). Training data with bi- or
  n-modal histograms in a single band are not
  ideal. In such cases the individual modes
  probably represent unique classes that should be
  trained upon individually and labeled as separate
  training classes.
• the probability of a pixel belonging to each of a
  predefined set of m classes is calculated based
  on a normal probability density function, and the
  pixel is then assigned to the class for which the
  probability is the highest. probability

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The estimated probability density function for
class wi (e.g., forest) is computed using the
equation where exp is e (the base of the
natural logarithms) raised to the computed power,
x is one of the brightness values on the x-axis,
is the estimated mean of all the values in
the forest training class, and is the
estimated variance of all the measurements in
this class. Therefore, we need to store only the
mean and variance of each training class (e.g.,
forest) to compute the probability function
associated with any of the individual brightness
values in it.
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Without Prior Probability Information Decide
unknown measurement vector X is in class i if,
and only if, pi gt pj for all i and j out of 1, 2,
... m possible classes and
The assign the measurement vector X of an unknown
pixel to a class, the maximum likelihood decision
rule computers the value pi for each class. Then
it assigns the pixel to the class that has the
largest value
Unless you select a probability threshold (0-1),
all pixels are classified. Each pixel is assigned
to the class that has the highest probability
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Probability threshold from 0, 1. 0 means zero
probability of similarity, 1 means 100
probability of similarity.
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2.4 Mahalanobis Distance

• M-distance is similar to the Euclidian distance

It is similar to the Maximum Likelihood
classification but assumes all class covariances
are equal and therefore is a faster method. All
pixels are classified to the closest ROI class
unless you specify a distance threshold, in which
case some pixels may be unclassified if they do
not meet the threshold (in DN number)
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2.5 Spectral Angle Mapper
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2.6 Spectral Feature Fitting

• compare the fit of image reflectance spectra to
  selected reference reflectance spectra using a
  least-squares technique. SFF is an
  absorption-feature-based methodology. Both
  reflectance spectra should be continuum removed.
• A scale image is output for each reference
  spectrum and is a measure of absorption feature
  depth which is related to material abundance. The
  image and reference spectra are compared at each
  selected wavelength in a least-squares sense and
  the root mean square (rms) error is determined
  for each reference spectrum.

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Least square tech (regression)
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• A continuum is a mathematical function used to
  isolate a particular absorption feature for
  analysis

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Supervised classification method Spectral
Feature Fitting
Source http//popo.jpl.nasa
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