View source for Refactoring of the classification chain

Currently, the
[http://wiki.orfeo-toolbox.org/index.php/Classification_chain classification tools] give very decent performances in
most cases with the default setup, which is nice, but they lack fine
tuning and flexibility especially for those samples related steps
before calling the training algorithms.

These are some of the issues that cuold be taken into account:

=== Sample source: ===
* images
* vector data (GIS files)
* CSV files
* other
=== Split sampling, sample normalisation, learning and validation (at the application level) ===
* needs the definition of sample I/O format and drivers
=== Define sample strategies ===
* raw vs balanced
* systematic (1/N) vs random vs stratified
* current one is balanced random
=== Allow for the generation of disjoint training and validation sample sets ===
* amount of overlap between the sets (usually 0)
* ratio between training and validation samples
* the validation training set may not need balancing even if the training one does
=== Define normalisation strategies ===
* centered-reduced
* min-max
* other 
=== Define stynthetic sample generation strategies ===
* add random noise
* combine exiting samples
* other