IGF::OT::IGF DSO SEEDLING THE PURPOSE OF THIS DSO SEEDLING EFFORT IS TO INVESTIGATE WHETHER NEW POLARIZATION SENSOR CONCEPTS INTEGRATED WITH A DEEP LEARNING ALGORITHM CAN LEAD TO CONSISTENT INTERNAL MODELS FOR IMAGING THROUGH FOG, THAT ARE SUFFICIENTLY GENERIC TO DEAL WITH A CLASS OF OBSCURED CONDITIONS, OR PROVIDE NEW INSIGHT ON A PHYSICAL MODEL THAT GOES BEYOND THE CURRENT STATE-OF-THE-ART, TO NUMERICALLY CORRECT IMAGES OF OBSCURED TARGETS. THIS EFFORT WILL FOCUS ON COLLECTING LARGE SETS OF SPATIALLY, TEMPORALLY, AND POLARIZATION RESOLVED IMAGES IN AN OBSCURED ENVIRONMENT, AND USING THE MEASURED DATA TO TRAIN A MACHINE LEARNING (ML) ALGORITHM. THE RESULTS WILL HELP DETERMINE IF A POLARIZATION PATTERN EXISTS WITH SUFFICIENT PRECISION AND ACCURACY TO PREDICT THE LOCATION OF AN UNSEEN OBJECT IN A FOGGY ENVIRONMENT BASED ON THE POLARIZATION RESOLVED MEASUREMENTS.