Evaluating Dataset-Aware Masked Supervision for Joint Segmentation of Heterogeneous Side-Scan Sonar Datasets

Joint segmentation of heterogeneous side-scan sonar datasets requires learning from different acoustic distributions and annotation scopes. Dataset-aware masked cross-entropy attenuates classes outside each dataset’s annotation scope, potentially reducing incorrect negative supervision while weakening cross-class competition. This study evaluates this trade-off using AI4Shipwrecks, MCM, and SubPipe in a shared four-class space. Experiments compare unmasked cross-entropy, soft attenuation, and hard exclusion across input representations and sampling policies. With five-channel SegFormer-B2, soft DMCE achieved a mIoU of 0.4908, below unmasked cross-entropy at 0.5234 in all five matched seeds; their foreground macro-IoU values were 0.0409 ± 0.0360 and 0.0694 ± 0.0413, respectively. After averaging the two repeated runs within each seed, hard masking achieved a descriptive macro mIoU of 0.1544 ± 0.0670 and a foreground macro IoU of 0.0144 ± 0.0153 across five seed-level summaries. Under PhysD input, the mean ordering reversed: soft DMCE achieved 0.5204 versus 0.4952 for cross-entropy. Dataset-balanced sampling increased cross-entropy performance to a macro mIoU of 0.5505 and a foreground macro IoU of 0.1138 ± 0.0144. Pipeline IoU nevertheless remained near zero despite SubPipe mIoU approaching 0.5, and held-out foreground IoU was zero when the only positive supervision for a category was excluded. Overall, the effect of attenuation varied across training configurations. These results show that background-inclusive macro mIoU should be interpreted alongside foreground-specific metrics when assessing joint segmentation performance.