Divisão de Observação da Terra e Geoinformática

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====== Meeting 02/03/2012 ======

== New results, using the seasonal feature of polygon area per quadrant ==

{{thales:4seasons.png?400|}}

Typology based on MOD12Q1 (2004-2005), using 11267 instances //(Water, Evergreen Broadleaf forest, Deciduous Broadleaf forest, Open shrublands, Woody savannas, Croplands)//

{{thales:kappas_mod12q1.png|}} {{thales:leaves_mod12q1.png|}}

Typology //(Single crop, Double crop, other classes)//

Using a combination of features (Basic + Polar), 90% of correct classified points (2074 instances).

{{thales:decision_tree_full_attributes.png?500|}}

== Results presented in paper ==

Basic features X Polar Features. Typology //(Single crop, Double crop, other classes)//

{{thales:mt_kappas_comparison.png}} {{thales:mt_complexity_comparison.png|}}

TerraClass (2008) X GeoDMA (growing season 2007-2008)

{{thales:geodma_x_terraclass_2008.png?400|}}

  scene 22469 with 58110 points, and 72% of correct classified points.
  scene 22670 with 13629 points, and 85% of correct classified points.
  scene 22768 with 83861 points, and 81% of correct classified points.
  scene 22969 with 10890 points, and 15% of correct classified points.
  scene 22769 with 60286 points, and 77% of correct classified points.
  scene 22668 with 65784 points, and 63% of correct classified points.
  scene 22770 with 29331 points, and 31% of correct classified points.
  scene 22669 with 97752 points, and 77% of correct classified points.

==== Thesis ====

//First Version//

**1 Introduction**
  * 1.1 Scientific Question and Hypothesis
  * 1.2 Contributions
  * 1.3 Document Organization

**2 Review**
  * 2.1 Landscape Ecology
  * 2.2 Object-Based Image Analysis
  * 2.3 Data Mining
  * 2.4 Multitemporal analysis

**3 Method**
  * 3.1 Defining the input data
    * 3.1.1 Segmentation
    * 3.1.2 Cycles detection
  * 3.2 Feature Extraction
    * 3.2.1 Segmentation-based
    * 3.2.2 Landscape-based features
    * 3.2.3 Multitemporal
      * 3.2.3.1 Basic features
      * 3.2.3.2 Linearity features
      * 3.2.3.3 Features based on polar representation
  * 3.3 Data mining to detect land change
    * 3.3.1 Building a reference set
    * 3.3.2 Classification using decision trees
  * 3.4 Output
    * 3.4.1 Measuring change

**4 Results**
  * 4.1 Land cover using intra-urban imagery
    * 4.3.1 Input dataset
    * 4.3.2 Experiment
  * 4.2 Detection of patterns of the urban landscape
  * 4.3 Classification of agriculture cycles
  * 4.4 Evaluating the new multitemporal features

**5 Conclusion**

//Alternative//

**1 Introduction**

**2 Paper GeoDMA**

**3 Paper Polar Features**

**4 ???**

**5 Conclusion**