Hey Jan, thanks for responding. I definitely do not want NaNs contaminating surrouding pixels at every convolutional layer. I was also thinking along the lines of cropping numerous sub-images or patches that contain only valid pixels. In this manner, I can:
1) Get rid of the NaN problem
2) Create a larger training set
Thanks, I will mark this question as complete.