Self-supervised training and evaluation workflow. (IMAGE)
Caption
Self-supervised training and evaluation workflow. During training, a random subset of valid observations is used as input together with an observation mask; the loss is computed only on valid (observed) locations to avoid supervising on missing pixels. During testing, a disjoint subset of valid observations is withheld as pseudo-ground truth, and metrics are computed only at these withheld locations. All preprocessing statistics (e.g., normalization and empirical orthogonal function [EOF] basis) are derived from the training period only to prevent data leakage.
Credit
Journal of Remote Sensing
Usage Restrictions
Credit must be given to the creator.
License
CC BY