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New Contributor
Posts: 3
Registered: ‎03-25-2018
Accepted Solution

Find total radiance per country across time (batch processing)

[ Edited ]

Hey guys,


Pretty new to ERDAS so I apologize in advance if the next few questions aren't asked using correct lingo.


I have images from here and a .shp file that contains boundaries and labels of all countries. I want to perform the following operations for each month from 4/12-3/18:


1) Mosaic the 6 tiles to create global image.

2) Calculate the total radiance per area per country.


I was able to do 1) using the mosaic tool, however I would like to automate this process if possible to batch process all the months together. I have not been able to get step 2) to work. At the moment I have the attached spatial model however it seems to get hung up and freezes (making me think I am not coding it correctly). Note that my raster image gives avg radiance values at each pixel and the .shp file gives countries with "ADMIM" being the unique country identifier. Any advice on how to get the average radiance per country to work would be much apprciated. Also a way to automate this over all months would be a real time saver (though not totally necessary).


Thanks in advance!



Technical Evangelist
Posts: 945
Registered: ‎10-01-2015

Re: Find total radiance per country across time (batch processing)



For issue #1 you will probably want to check out this Spatial Recipe:


For issue #2, are you selecting the Statistics you actually want in the Raster statistics per Feature operator? The default is Mean, but you have lots of other stats you can derive such as Sum. Also be aware that the  field you have set to "ADMIN" is defining the output attribute field name to store the statistic. It is not selecting the input geometries to be processed. But that shouldn't be preventing the model from running - it's just going to use that as the base attribute field name.


You do need to specify a name for the file produced by Features Output.


If the input raster is a big file then it may well take some time to calculate the results. Might be worth trying some smaller datasets first to check you've got the logic correct before turning the model lose on the full data.





Ian Anderson
Chief Product Owner, Desktop Remote Sensing
Hexagon Geospatial