I am looking for robust ways to fill in missing values in some rasters. They all have a single layer. Missing values consist of single pixels to medium sized patches. Rasters are around 1000 x 1000 pixels in size and the largest patches are like 20x20 pixels.
I'm tempted to use aregImpute in the Hmisc R package. Has anyone used it for this purpose?
this approach looks seems very cool but I think it is only meant to produce aesthetically pleasing corrections.
Detailed explanation of this:
All the rasters (I have 36 in total) share the same extent, they overlap and are aligned. Each raster is a different variable, I gathered variable from various sources (remote sensing, topographic and climatological). The original rasters come in various resolutions. The smallest being 30m. From there up they get as high as 1km. I resampled everything using cubic convolution (all the variables are continuos) to 1km. I have another 1km raster where I have data of a variable of interest for some sampled points. So I trained a model using those points and the other rasters as covariates to be able to generate a full raster of that variable. Unfortunately most covariate rasters have some missing values in them, actually not much but I would want to eliminate the problem entirely.
p.s. I would preferably like to use R for this.