Kevin_Sanchez:

If my channels have different value ranges (like 100-150 vs 300-400) should I normalize each channel by the range of that channel or the range of all the channels? I think the latter?


If the ranges are “similar” it porobably doesn’t matter. But if the difference is large, I’d normalize by the channel’s individual range.



 Kevin_Sanchez:

The range of values in the actual images is about 100-400. So I could set the blank pixels to zero then normalize or 100 then normalize. I am not sure it matters.


What?



 Kevin_Sanchez:

how to handle blank space





 Kevin_Sanchez:

There is only 1 feature/class I am trying to model


It sounds like you just need  a binary image for the labels, and a logit-image for the predictions.