I have a complex classification problem to solve:
I have multiple shapefiles containing the polygons of all the parcels of different kinds e.g. a shapefile with all polygons with maize, another with rice etc
I have a time series multispectral imagery of those parcels and indexes e.g.NDVI calculated
I have made a shapefile with all the training polygons of the classes needed to describe the parcels according to their description: 10 polygons maize, 10 rice etc.
I have constructed a signature file with the above training polygons merged accordingly to the classes, containing all the different layers' mean values: Layers-> All the NDVI rasters, R,G,B layers from all times
Now with these components -or with others if suggested so-, I need to classify all the parcels to a class according to the signature. For the sake of example lets assume I have 10000 parcels, 20 classes/cultivation types and 30 layers. As a result the Signature file has "size" 20x30.
I need a supervised classification approach, like Maximum Likelihood.
What can I do to assign each parcel to a class?