I am going to perform an unsupervised classification on my dataset and will get a map as a result. The objective is to rebuild an already existing map (of different soil types) with my new dataset. Unfortunately I don't have any underlying data information to the existing map - just the polygon features and their different classes.
My problem is: How can I validate which unsupervised classification result has a better correlation with the existing map?
The main issue will be in comparing the spatial patterns of the polygons and thereby derive which classification result represents the existing map better. (I don't want to make just a visual comparison, but an analytic- mathematical one.)
My dataset from where I will perfom the unsupervised classification is raster based. The other (already existing) map consists of polygons (in shapefiles) with nothing more than the information of which type of class has been assigned.
I'd be happy if you could help me out with a proper methodology or process. (I am working either in R, QGIS and ARCGis).
After some research I came across a very promissing statistical approach: the Rand index.
In R there are various implementations (package
flexclust) - I will work them through in the next days and hopefully can deliver more information on the topic in a while.