Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning

arXiv:2604.21349v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is a standard approach for representation learning in aerial imagery. Existing methods enforce invariance between augmented views, which works well when augmentations preserve semantic content. However, aerial images are frequently degraded by haze, motion blur, rain, and occlusion that remove critical evidence. Enforcing alignment between a clean and a severely degraded view can introduce spurious structure into th