Effects of Loss Function Choice on One-Shot HSI Target Detection With Paired Neural Networks

Implementing reliable few-shot capable classifiers and detectors in machine learning is no trivial task and often requires parsing a large set of hyperparameters and training routine choices to find the best fit. One such choice is the loss function itself. In this effort, we study the validation and test performance of paired neural network (PNN) architectures using contrastive, hard-triplet, and semihard-triplet losses. These are tested by training multiple models to perform one-shot target detection on a custom synthetic hyperspectral image (HSI) dataset with and without reflectance calibration. We find that no single loss function is superior across all data treatments and standard scoring metrics can even disagree among the loss function choice among differing train, validation, and test split choices. We additionally analyze differences in detection map quality for selected test examples illustrating that while most are useful, some models will have more intuitive detection thresholds. Our work suggests multiple loss functions should be considered each time a new dataset and task are encountered to train PNNs for HSI target detection. These findings indicate significant variability in one-shot target detection performance based on the combination of training loss and data treatment but suggest the semihard-triplet loss, combined with a relatively simple reflectance calibration of the imagery, tends to generalize best across the common set of target materials studied.