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--- interimage:examples:supervised_c45 [2010/06/18 19:03] – created tkorting
+++ interimage:examples:supervised_c45 [2010/09/14 14:42] (atual) – tkorting
@@ Linha 1: / Linha 1: @@
 ====== Supervised Classification Using Decision Trees ======
-InterIMAGE performs supervised classification through the C4.5 decision tree classifier. This example shows how to perform a supervised classification, using the Top Down operator //TerraAIDA_C45_Classifier//.
+InterIMAGE performs supervised classification through the C4.5 decision tree classifier. This example shows how to perform a supervised classification, using the Top Down operator //TA_C45_Classifier//.
 ===== Download =====
-???
+{{:interimage:examples:ta_interimage_examples_supervised_c45.zip}}
 ===== Step-by-step =====
@@ Linha 11: / Linha 11: @@
 In order to perform classification, the user must train the algorithm by providing samples of every class. In this way, the classification algorithm creates a decision tree and applies it into the set of objects, classifying them.
-Firstly define your interest classes, and create nodes for them. All nodes must be in the same level, only one of them must have the //TerraAIDA_C45_Classifier// as Top Down Operator, and all the remaining nodes must have //No Operator//. Note: your interest classes must be with the option **TopDown Multi-Class** selected.
+Firstly define your interest classes, and create nodes for them. All nodes must be in the same level, only one of them must have the //TA_C45_Classifier// as Top Down Operator, and all the remaining nodes must have //No Operator//. Note: your classes of interest must be with the option **TopDown Multi-Class** selected.
-To perform the training step, you must enter the **Samples Editor**. Firstly select the node whose childs are the interes classes, and open the editor as in the figure:
+{{:interimage:examples:supervised_c45_nodes.png?600}}
+To perform the training step, you must enter the **Samples Editor**. Firstly select the node whose childs are the classes of interest, and open the editor as in the figure:
 {{:interimage:examples:samples_editor.png?600}}
-Select one operator to perform segmentation (//i.e. TerraAIDA_Baatz_Segmenter//), and press //Segment//. After this, when the objects appear inside the image, select one interest class, and press //Collect Samples//. Then select all your samples to the interest class, and press //Collect Samples// again to stop the selection. Perform this for all your interest classes.
+Select one operator to perform segmentation (//i.e. TA_Baatz_Segmenter//), and press //Segment//. Afterwards, when the objects appear inside the image, select one interest class, and press //Collect Samples//. Then select all your samples to the interest class, and press //Collect Samples// again to stop the selection. Perform this for all your interest classes.
 Note that your selected objects will be highlighted with the corresponding class:
-{{:interimage:examples:samples_selection.png?600}}
+{{:interimage:examples:samples_selection.png?400}}
 After you selected your samples, you can export one shapefile with the training set, and all remaining elements. Press //Export// and one window to define attributes will open. Create your expressions to define your desired attributes, and select the shape file name to be exported:
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 {{:interimage:examples:importing_shapefile.png?400}}
+After defining all parameters, you can run the project. The resultant classification will be as follows:
+{{:interimage:examples:supervised_c45_result_correct.png?600}}
+The generated decision tree will be placed in a //.txt// file, located in the same place as your input shape file, and will have the following content:
+  RATIO_2 >  0.4  -> roofs
+  RATIO_2 <=  0.4
+  |   RATIO_2 <=  0.3  -> pools
+  |   RATIO_2 >  0.3  -> background
+Note that the automatic classification generated some wrong hypothesis, so you can create further rules to increase the classification accuracy.