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| ====== Supervised Classification Using Decision Trees ====== | ====== 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 ===== | ===== Download ===== | ||
| - | ??? | + | {{:interimage:examples:ta_interimage_examples_supervised_c45.zip}} |
| ===== Step-by-step ===== | ===== Step-by-step ===== | ||
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| 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. | 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}} |
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| + | 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}} | {{: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: | Note that your selected objects will be highlighted with the corresponding class: | ||
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| {{:interimage:examples:importing_shapefile.png?400}} | {{: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}} | ||
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| + | 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. | ||
