In the latest scenProc development release there are a number of changes to how the machine learning steps work. This post gives an overview of them:

• Besides the SVM machine learning step that has been available for a while, a new one has been added now. This one uses the Artificial Neural Networks – Multi-Layer Perceptrons (MLP) algorithm for the machine learning.
  While working on detecting of water data, I had the impression this algorithm gives better results. But I’m not an expert on the differences of these steps and they both have a lot of attributes to tune them. So I guess you best see for yourself which one works best for you.
• When you change attributes of a machine learning step or add sample points, the machine learning algorithm is not trained automatically every time. This was quite annoying as the training can take long with many sample points. Now the step will be rendered in red in the step overview and you have to manually trigger the retraining of the step with a button on the toolbar when you are ready for it.
• Besides training machine learning steps with sample points, it is now also possible to train them with raster data. So if you have reference raster data of the required classification or if you have good vector data so that you can make such reference data you can use this as an alternative way to train the algorithm. The machine learning steps have two optional inputs for this, for the labels and the reference data. If any of those two inputs are not connected in your filter the sample points are used for training. Be aware that training on raster data typically takes longer as it includes a lot more training data.