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UTD-GeospatialMining-Dec2006

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									 Geospatial Data Mining at
University of Texas at Dallas
 Dr. Bhavani Thuraisingham (Computer Science)
      Dr. Latifur Khan (Computer Science)
              Dr. Fang Qiu (GIS)


                   Students
              Shaofei Chen (GIS)
           Mohammad Farhan (CS)
              Shantnu Jain (GIS),
                Lei Wang (CS)


                  Post Doc:
               Dr. Chuanjun Li
  This Research is Partly Funded by Raytheon
Outline

   Case Study
     - ASTER Dataset
     - Technical Challenges
     - Sketches
   Process of Our Approach
     - Pixel classification using SVM Classifiers
     - Ontology Driven Mining
             Pixel Merging
   Output
   Related Work
   Future Work
Case Study: Dataset
  ASTER (Advanced Spaceborne Thermal Emission and
   Reflection Radiometer)
     - To obtain detailed maps of land surface temperature,
       reflectivity and elevation.
  ASTER obtains high-resolution (15 to 90 square meters per
   pixel) images of the Earth in 14 different wavelengths of the
   electromagnetic spectrum, ranging from visible to thermal
   infrared light.
  ASTER data is used to create detailed maps of land surface
   temperature, emissivity, reflectivity, and elevation.
Case Study: Dataset & Features
  Remote sensing data used in this study is ASTER image
   acquired on 31 December 2005.
     - Covers northern part of Dallas with Dallas-Fort Worth
       International Airport located in southwest of the image.
  ASTER data has 14 channels from visible through the thermal
   infrared regions of the electromagnetic spectrum, providing
   detailed information on surface temperature, emissive,
   reflectance, and elevation.
  ASTER is comprised of the following three radiometers :
    -  Visible and Near Infrared Radiometer (VNIR --band 1
       through band 3) has a wavelength range from
       0.56~0.86μm.
Case Study: Dataset & Features
  Short Wavelength Infrared Radiometer (SWIR-- band 4
   through band 9) has a wavelength range from 1.60~2.43μm.
     - Mid-infrared regions. Used to extract surface features.
  Thermal Infrared Radiometer (TIR --band 10 through band 14)
   covers from 8.125~11.65μm.
     - Important when research focuses on heat such as
       identifying mineral resources and observing atmospheric
       condition by taking advantage of their thermal infrared
       characteristics.
ASTER Dataset: Technical Challenges
  Testing will be done based on pixels
  Goal: Region-based classification and identify high level
   concepts
  Solution
    -  Grouping adjacent pixels that belong to same class
     - Identify high level concepts using ontology-based mining
Sketches: Process of Our Approach
          Training    Feature      Features     Classifier     SVM
           Data       Extraction   (14/pixel)   Training     Classifiers



  ASTER    Test       Feature      Features     Validation
  Image    Data       Extraction   (14/pixel)



          All Pixel                Features     Classification
                      Feature
           Data                    (14/pixel)
                      Extraction



                                                    Pixel Grouping
                          High Level Concepts
Process of Our Approach
                                      Testing Image Pixels


    Training Image Pixels       SVM Classifier
                                      Classified Pixels


                                 Pixel Merging

                                      Concepts and Classes




                            Ontology Driven Mining


                                          High Level Concepts
SVM Classifiers: Atomic Concepts

    Classes                                      Train set       Test set



    Water                                        1175            1898

    Barren Lands                                 1005            1617

    Grass                                        952             1331

    Trees                                        887             1479

    Buildings                                    1041            768

    Road                                         435             648

    House                                        1584           1364

    # of instances                               7079           9105

                Different Class Distribution of Training and Test Sets
Process of Our Approach
                                      Testing Image Pixels


    Training Image Pixels       SVM Classifier
                                      Classified Pixels


                                 Pixel Merging

                                      Concepts and Classes




                            Ontology Driven Mining


                                          High Level Concepts
Ontology-Driven Mining
    -   Ontology will be represented as a directed acyclic graph (DAG). Each node in
        DAG represents a concept
    -   Interrelationships are represented by labeled arcs/links. Various kinds of
        interrelationships are used to create an ontology such as specialization (Is-a),
        instantiation (Instance-of), and component membership (Part-of).
                                                        IS-A
                                                                        Urban


                                   Residential


                   Part-of


                                      Single Family             Multi-family
             Apartment
                                          Home                    Home
Ontology-Driven Mining
  We will develop domain-dependent ontologies
    - Provide for specification of fine grained concepts
    - Concept, “Residential Area” can be further categorized
      into concepts, “House”, “Grass” and “Tree” etc.

  Generic ontologies provide concepts in coarser grain
Ontology Driven Mining

                             Target Area




       Urban Area          Residential Area        Open Area




 Building   Road    Tree       House       Grass   Water   Barren Land
Challenges
  Region growing
    - Find out regions of the same class
    - Find out neighboring regions
    - Merge neighboring regions
    - Not scalable
         Irregular  regions
          Of different sizes
          Hard to track boundaries or neighboring regions
  Pixel merging
     - Only neighboring pixels considered
     - Pixels are converted into Concepts
     - Linear
Pixels Merging
Pixels Merging
Complexity
  There are two iterations:
    - First iteration converts signature classes into Concepts
    - Second iteration converts remaining classes and isolated
       concepts into Dominating classes
  Each pixels take O(1) time
  Target area takes O(n) time, where n is the number of pixels in
   the target area
  Example (next slide):
     - Signature classes: c1, c2, c3
    -  Non-signature class: c4
     - Concepts: C1, C2, C3
Pixels Merging

c1   c1         c2   c2   C2   c1     c2   c2   C2   c1         c2   c2

c1   c3         c2   c2   c1   c3     c2   c2   C2   c3         c2   c2

c2   c3         c2   c2   c2   c3     c2   c2   c2   c3         c2   c2

c3   c3         c2   c3   c3   c3     c2   c3   c3   c3         c2   c3

c3   c4         c3   c3   c3   c4     c3   c3   c3   c4         c3   c3

c4   c4         c3   c3   c4   c4     c3   c3   c4   c4         c3   c3

          (a)                       (b)                   (c)



C2   c1         c2   c2   C2   c1     c2   c2   C2   c1         c2   c2

C2   c3         c2   c2   C2   c3     c2   c2   C2   c3         c2   c2

C2   c3         c2   c2   C2   c3     c2   c2   C2   c3         c2   c2

c3   c3         c2   c3   C3   c3     c2   c3   C3   c3         c2   c3

c3   c4         c3   c3   c3   c4     c3   c3   C3   c4         c3   c3

c4   c4         c3   c3   c4   c4     c3   c3   c4   c4         c3   c3

          (d)                       (e)                   (f)
Implementation


  Software:
    - ArcGIS 9.1 software.
    - For programming, we use Visual Basic 6.0 embedded in the
      software.
Output:
Output
Output
Related Work
  Classification (SVM)
        Farid Melgani, Lorenzo Bruzzone, Classification of
         hyperspectral remote-sensing images with support
         vector machines.
        Zhu, G. and D.G. Blumberg. (2002). Classification
         using ASTER data and SVM algorithms - The case
         study of Beer Sheva, Israel.
        Huang C.; Davis L. S.; Townshend J. R. G. (2002) An
         assessment of support vector machines for land
         cover classification.
Future Work
   Develop Full Fledged Prototype (By January 31, 2007)
   Generate Rules automatically (By June 30, 2007)
     - Ripper–Semi-automatically
     - Association mining

								
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