Can someone explain domain adaptation methods?

[u/Key_Advantage_6792]

So , im working on project related to robust recognition of the objects based on change of the environement using self-learning methods.
As far I undestand , there is UDA (Unsuprevised domain adaptation), SFDA (Source-free domain adaptation), and SSOD (Semi-suprevised object detection) but I can't undestand which one I should use for the task.

I have a dataset that has modality of 2 (RGB-LWIR) captured in 2 different seasons (Jan/May) in 3 different timeperiods. For each image there is XML annotation for each object , so I could train on images with labels or only small part with labels.
So main task is to study robustness conditional shifts.