[GIS] Extract All Pixel Values for each band in a Multi Band Raster

arcgis-10.0arcgis-desktopextractmulti-bandpixel

I have a few multi band rasters I'm working with, and I need to perform statistical analysis (in another program) on the data. My mentor told me to determine the value for each pixel in each band, and from there move to the other program. But I'm not sure how to go about this. I've tried a lot of things, including pixel inspector and zonal statistics, but I don't think either of these give me exactly what I'm looking for. Does anyone have any other ideas?

Best Answer

If I understand correctly you can accomplish this manually by using a tool such as Composite Bands: http://help.arcgis.com/EN/ARCGISDESKTOP/10.0/HELP/index.html#//00170000009p000000

Go into ArcCatalog and expand the multi band raster dataset (clicking the "plus" icon) and drag a single band into the input dialog for Composite Bands. Make sure the one band is the only input. Once run you will get a new single band raster dataset.

Then just repeat with the other bands.

Once you have all of the bands separated use raster to raster conversion tools to convert them to whatever format the other software you are using can consume (probably GeoTiff or ASCII). What software is it? Perhaps we could assist further in determining format.

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[GIS] Determining statistical relationships between rasters using R vs ArcGIS Desktop

I would stick to R. If speed is really a problem ( I doubt so 90.000 is not such a big number) you could try finding relationships between a subset of your data. Actually the first thing I would do is make a plot to look for obvious relationships.

Even if arcgis contains tools to compare rasters, R will always give you a lot more statistical tools.

Eg:

library(rgdal)
map1<-readGDAL('file.asc')
map2<-readGDAL('file2.asc')
samplenr<-sample(length(map1$band1), 1000)
smallset<-data.frame(map1=map1$band1[samplenr],map2=map2$band1[samplenr])
plot(smallset)
lm(map2~map1, smallset)
...

I should actually add that often it is more correct to work with a subset of your data then with your full dataset. In many cases grid cells are not independent from the surrounding data cells, which will result in overly optimistic p values for eg regression fits (you will find more info if you search on declustering).

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