Have you read the paper describing the multi-scale classification ? It's probably the best way to start and understand how it works.
Can you give me a list of other questions you have on Canupo. I'll try to compile them and create a FAQ. I'll also make a tutorial video in the coming days/week.
About the scales: check figure 2 in the paper http://arxiv.org/pdf/1107.0550.pdf
. One scale is the diameter of the ball centered on the point that is being classified. When you want to create a classifier, you specify a series of scales that corresponds to the distances around a point that will be used to characterize the local geometry of the cloud.
For instance: think of the difference between a flat wall and a vegetation bush. If your point cloud has a typical point spacing of about 1 cm, at scales of 5 cm, the wall appear 2D (i.e. a plane) while vegetation is made of 2D objects (leaves) and 1D objects (lines = stems). At this small scale, leaves could be mistaken for a wall, and vice-versa, so you need to use a larger scale. At a scale of 50 cm, the wall is still a plane and thus a 2D structure, while vegetation now appears has a bunch of points spread out in 3D. THis is a scale at which the wall and the vegetation is very different. Now, because it is not necessarily easy to pick the scales at which objects are the most different manually, and because various scales can help in the classification, you can specify a range of scales (in that case from ~ 5 cm to 50 cm with 5 cm intervals), and let qCANUPO-training find the best combination of scales that will allow the distinction between vegetation and wall.
Tip : do not use scales that are much larger than the type of objects you want to classify, because:
1. the largest is the maximum scale, the longer will be the computation time (and it increases non-linearly)
2. your classifier will loose spatial resolution
This is why the qCANUPO training plugin allow you to dynamically remove the largest scale to see if the classification result is still ok (i.e. blue and red points are well separated by the line). It's a neat way to optimize the resolution and computation time for a classifier.
Hope this help