InterIMAGE Examples: Supervised Classification Using Decision Trees

This example shows how to perform a supervised classification of pools and roofs in a small image, through the C4.5 decision-tree classifier.

The InterIMAGE interpretation project can be downloaded here: download

The image used in this example is the following:

InterIMAGE performs supervised classification through the C4.5 decision tree classifier.

In order to perform the classification, the user must train the algorithm by providing samples of every class. In this way, the classification algorithm is able to create a decision tree and apply it into the whole set of objects, classifying them.

Here is an example of training file, in xml format:

File training_set.xml

<region class="roofs" ratio_b1="0.409163" />
<region class="roofs" ratio_b1="0.405262" />
<region class="pools" ratio_b1="0.293273" />
<region class="pools" ratio_b1="0.236392" />
<region class="background" ratio_b1="0.328659" />
<region class="background" ratio_b1="0.333080" />

Such file contains the information that two elements from class roofs have the attribute ratio_b1 with values “0.409163” and “0.405262”, respectively. The same happends for classes pools and background.

On the InterIMAGE interface, the user must create such classes, and set the property TopDown Multi-Class as true.

One of the nodes should perform segmentation and attributes extraction. This is done by the node pools, like the following figure.

After creating all classes present in the xml training file, setting all parameters, you can start your process, and visualize the results.

Future versions of InterIMAGE will have the interface for training.