Composite Raster in Deep Learning

Ed_

Hello,

Let's say there are 4 different types of rasters (three are DEM derived and one is NDVI), A, B, C and D, and use the `Composite Raster` tool to create a composite raster from these four rasters, that will later used to train a Deep Learning model and again used as input raster in `Classify Pixels using Deep Learning`.

Does the order of raster variables in the composite raster make any difference when using Deep Learning?

Pro version 3.4.2 and model in question in PSPNet and UNet.

PavanYadav

hi @Ed_ if you trained a model with 1,4,2,3; for inferencing you will need have the same combination. For any reasons if you need to retrain this model; you will need to create training data with the same combination e.g. 1,4,2,3.. 

But, correct me if I am mistaken -- I guess you're asking if there would be any difference if you have 1,2,3,4 or 3,4,1,2? I cannot say for sure because it's something I have not investigated much. Theoretically, it may impact the learning.. deep learning models, particularly CNNs, handle each band as an individual 'channel.' The model learn to identify patterns and features depending on the specific sequence of these channels. Consequently, the way the model interprets the data might be directly affected by the order in which the rasters are arranged. You might want to experiment with different combinations to see which one yields better results