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GEOMETRIC CORRECTION

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									Thematic Information Extraction:
     Pattern Recognition



          Chapter 9
       Classification

Multispectral classification may be performed using a variety of methods,
  including:

• algorithms based on parametric and nonparametric statistics that use
  ratio- and interval-scaled data and nonmetric methods that can also
  incorporate nominal scale data;
• the use of supervised or unsupervised classification logic;,
• the use of hard or soft (fuzzy) set classification logic to create hard or
  fuzzy thematic output products;
• the use of per-pixel or object-oriented classification logic, and
  hybrid approaches.
        Classification
• Parametric methods such as maximum likelihood classification and
  unsupervised clustering assume normally distributed remote sensor data and
  knowledge about the forms of the underlying class density functions.

• Nonparametric methods such as nearest-neighbor classifiers, fuzzy classifiers,
  and neural networks may be applied to remote sensor data that are not normally
  distributed and without the assumption that the forms of the underlying
  densities are known.

• Nonmetric methods such as rule-based decision tree classifiers can operate on
  both real-valued data (e.g., reflectance values from 0 to 100%) and nominal
  scaled data (e.g., class 1 = forest; class 2 = agriculture).
         Supervised Classification

   In a supervised classification, the identity and location of some of the land-
    cover types (e.g., urban, agriculture, or wetland) are known a priori through a
    combination of fieldwork, interpretation of aerial photography, map analysis,
    and personal experience.
   The analyst attempts to locate specific sites in the remotely sensed data that
    represent homogeneous examples of these known land-cover types.
   These areas are commonly referred to as training sites because the spectral
    characteristics of these known areas are used to train the classification
    algorithm for eventual land-cover mapping of the remainder of the image.
   Multivariate statistical parameters (means, standard deviations, covariance
    matrices, correlation matrices, etc.) are calculated for each training site. 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

   In an unsupervised classification, the identities of land-cover
    types to be specified as classes within a scene are not generally
    known a priori because ground reference information is lacking
    or surface features within the scene are not well defined.

   The computer is required to group pixels with similar spectral
    characteristics into unique clusters according to some statistically
    determined criteria.

   The analyst then re-labels and combines the spectral clusters into
    information classes.
        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 of the real world.
       Per-pixel 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
  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).
      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, human-made
  materials such as asphalt).

•    Land use refers to what people do on the land surface (e.g.,
    agriculture, commerce, settlement).
       Land-use and Land-cover Classification Schemes

• Mutually exclusive means that there is no taxonomic overlap (or fuzziness) 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. This allows simplified
  thematic maps to be produced when required.
     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.
          Land-use and Land-cover Classification Schemes

Certain hard classification schemes can readily incorporate land-use and/or land-cover data
   obtained by interpreting remotely sensed data, including the:

•    American Planning Association Land-Based Classification System which is oriented
    toward detailed land-use classification;

•   United States Geological Survey Land-Use/Land-Cover Classification System for Use
    with Remote Sensor Data and its adaptation for the U.S. National Land Cover
    Dataset and the NOAA Coastal Change Analysis Program (C-CAP);

•   U.S. Department of the Interior Fish & Wildlife Service Classification of Wetlands and
    Deepwater Habitats of the United States;

•   U.S. National Vegetation and Classification System;

•   International Geosphere-Biosphere Program IGBP Land Cover Classification System
    modified for the creation of MODIS land-cover products
U.S. Geological Survey Land-
Use/Land-Cover Classification
System
Four Levels of the U.S.
Geological Survey Land-
Use/Land-Cover
Classification System for
Use with Remote Sensor
Data and the type of
remotely sensed data
typically used to provide
the information.
       Selecting the Optimum Bands for Image Classification:
       Feature Selection

• Once the training statistics have been systematically collected from each band
  for each class of interest, a judgment must be made to determine the bands
  (channels) that are most effective in discriminating each class from all others.
• This process is commonly called feature selection. The goal is to delete from
  the analysis the bands that provide redundant spectral information. In this way
  the dimensionality (i.e., the number of bands to be processed) in the dataset
  may be reduced.
• This minimizes the cost of the digital image classification process (but should
  not affect the accuracy). Feature selection may involve both statistical and
  graphical analysis to determine the degree of between-class separability in the
  remote sensor training data.
• Using statistical methods, combinations of bands are normally ranked
  according to their potential ability to discriminate each class from all others
  using n bands at a time.
          Select the Appropriate Classification Algorithm

•   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.
      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.
         Unsupervised Classification

• Unsupervised classification is the process where numerical operations are
  performed that search for natural groupings of the spectral properties of pixels,
  as examined in multispectral feature space.

• The clustering process results in a classification map consisting of m spectral
  classes. The analyst then attempts a posteriori (after the fact) to assign or
  transform the spectral classes into thematic information classes of interest (e.g.,
  forest, agriculture).

• This may be difficult. Some spectral clusters may be meaningless because they
  represent mixed classes of Earth surface materials. The analyst must understand
  the spectral characteristics of the terrain well enough to be able to label certain
  clusters as specific information classes.
         Unsupervised Classification

Hundreds of clustering algorithms have been developed. Two
  examples of conceptually simple but not necessarily efficient
  clustering algorithms will be used to demonstrate the fundamental
  logic of unsupervised classification of remote sensor data:

•    clustering using the Chain Method
•    clustering using the Iterative Self-Organizing Data Analysis
    Technique (ISODATA).
       Clustering Using the Chain Method

The Chain Method clustering algorithm operates in a two-pass mode (i.e., it passes
   through the multispectral dataset two times).

Pass #1: The program reads through the dataset and sequentially builds clusters
   (groups of points in spectral space). A mean vector is then associated with
   each cluster.

Pass #2: A minimum distance to means classification algorithm is applied to the
   whole dataset on a pixel-by-pixel basis whereby each pixel is assigned to one
   of the mean vectors created in pass 1. The first pass, therefore, automatically
   creates the cluster signatures (class mean vectors) to be used by the minimum
   distance to means classifier.
        ISODATA Clustering

• The Iterative Self-Organizing Data Analysis Technique (ISODATA) represents
  a comprehensive set of heuristic (rule of thumb) procedures that have been
  incorporated into an iterative classification algorithm. Many of the steps
  incorporated into the algorithm are a result of experience gained through
  experimentation.

• The ISODATA algorithm is a modification of the k-means clustering algorithm,
  which includes a) merging clusters if their separation distance in multispectral
  feature space is below a user-specified threshold and b) rules for splitting a
  single cluster into two clusters.
        ISODATA Clustering

• ISODATA is iterative because it makes a large number of passes through the
  remote sensing dataset until specified results are obtained, instead of just two
  passes.

•    ISODATA does not allocate its initial mean vectors based on the analysis of
    pixels in the first line of data the way the two-pass algorithm does. Rather, an
    initial arbitrary assignment of all Cmax clusters takes place along an n-
    dimensional vector that runs between very specific points in feature space. The
    region in feature space is defined using the mean, µk, and standard deviation,
    sk, of each band in the analysis. This method of automatically seeding the
    original Cmax vectors makes sure that the first few lines of data do not bias the
    creation of clusters.
         ISODATA Clustering

ISODATA is self-organizing because it requires relatively little human input. A
   sophisticated ISODATA algorithm normally requires the analyst to specify the
   following criteria:

•    Cmax: the maximum number of clusters to be identified by the algorithm (e.g.,
    20 clusters). However, it is not uncommon for fewer to be found in the final
    classification map after splitting and merging take place.

•    T: the maximum percentage of pixels whose class values are allowed to be
    unchanged between iterations. When this number is reached, the ISODATA
    algorithm terminates. Some datasets may never reach the desired percentage
    unchanged. If this happens, it is necessary to interrupt processing and edit the
    parameter.
        ISODATA Clustering

• M: the maximum number of times ISODATA is to classify pixels and
  recalculate cluster mean vectors. The ISODATA algorithm terminates when
  this number is reached.

• Minimum members in a cluster (%): If a cluster contains less than the minimum
  percentage of members, it is deleted and the members are assigned to an
  alternative cluster. This also affects whether a class is going to be split (see
  maximum standard deviation). The default minimum percentage of members is
  often set to 0.01.
Analog and
Digital
Image
Analysis
Tasks
        Object-oriented Image Segmentation

• This need has given rise to the creation of image classification algorithms based
  on object-oriented image segmentation. The algorithms incorporate both
  spectral and spatial information in the image segmentation phase.
• The result is the creation of image objects defined as individual areas with
  shape and spectral homogeneity which one may recognize as segments or
  patches in the landscape. In many instances, carefully extracted image objects
  can provide a greater number of meaningful features for image classification.
• In addition, objects don’t have to be derived from just image data but can also
  be developed from any spatially distributed variable (e.g., elevation, slope,
  aspect, population density).
• Homogeneous image objects are then analyzed using traditional classification
  algorithms (e.g., nearest-neighbor, minimum distance, maximum likelihood) or
  knowledge-based approaches and fuzzy classification logic.
        Object-oriented Image Segmentation

• There are many algorithms that can be used to segment an image into relatively
  homogeneous image objects. Most can be grouped into two classes:
   • edge-based algorithms, and
   • area-based algorithms.

• Unfortunately, the majority do not incorporate both spectral and spatial
  information, and very few have been used for remote sensing digital image
  classification.
        Object-oriented Image Segmentation

One of the most promising approaches to remote sensing image segmentation was
  developed by Baatz and Schape (2000). The image segmentation involves
  looking at individual pixel values and their neighbors to compute a (Baatz et.
  al., 2001):

    •   color criterion (hcolor), and

    •   a shape or spatial criterion (hshape).
    Object-oriented Image Segmentation

• The object-oriented classification of a segmented image is substantially
  different from performing a per-pixel classification.
• First, the analyst is not constrained to using just spectral information. He or
  she may choose to use
         a) the mean spectral information in conjunction with
         b) various shape measures associated with each image object
  (polygon) in the dataset.
• This introduces flexibility and robustness. Once selected, the spectral and
  spatial attributes of each polygon can be input to a variety of classification
  algorithms for analysis (e.g., nearest-neighbor, minimum distance,
  maximum likelihood).

								
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