Classifier (Supervised Classification)
Supervised classification is probably the most commonly used machine learning technique. The 'classifier' algorithm offers supervised classification in eCognition and there are many 'classifier' parameters available to support several use cases: eCognition users can perform object- or a pixel-based training & classification, furthermore the users can load training samples from vector or statistics layers, and last but not least several classification methods (Decision Tree/CART, Random Trees/Random Forest, Bayes, KNN or SVM) be at user’s command.
Some parameter combination supports feature space optimization or to convert a trained model into ruleset code.
To get not lost in all the possibilities, Christian Weise generated (in 2012) the 'Example Project - Classifier (Supervised Classification)' to demonstrate typical supervised classification scenarios in eCognition.
Download 'Example Project - Classifier (Supervised Classification)'
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- Tree Crown Delineation
- DTM Modelling of Open-pit Mines
- Vineyard Canopy Mapping
- Classifier (Supervised Classification)
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- Stddev Split Segmentation
- ESP II: Automated Estimation of Scale Parameter (ESP2) Tool
In the example project there are one image and 2 shapefiles. Where is the eCognition project (dpr)?
Please try to download again. In the zip file you will find the input data (image + shapefile) as well as the Project.dpr.
Thank you very much!