I have a georeferenced RGB raster that I am trying to digitise (see attachment 1). I have had success vectorising the contours using arcscan, however this is a time consuming process and it is still missing some of the detail contained within the colourfil grid. It is this additional detail I am essentially hoping to capture in a methodology that is easily repeatable across many input maps.
To try and break the task in to steps, I think I need to:
1) Sample the numbered breaks on the colour bar provided on the map (at bottom left of attachment, ranging from 2216 to 5452)
2) Interpolate the known sample numbers to account for the colours in between
3) Reclassify the RGB image using the calibration above
I have had some partial success by using the training sample manager, carefully selecting sample colours next to the colour bar labels resulting in 13 points and then using 'Maximum Likelihood Classification'. However these labeled points also correspond with the black contour breaks and so I'm not getting many reclassified points..
So, assuming my rough idea of methodology is along the right lines, I need to work out how to take more samples along the RGB colour bar, calibrate the known values and then interpolate in between to then be able to apply the Maximum Likelihood Classification.
Has anyone attempted this before? Or perhaps have any ideas on my proposed work flow or how to achieve what I'm attempting to do?
Many thanks in advance for any feedback you may have.