This research paper introduces a new approach to adapting deep learning models for underwater synthetic aperture sonar Automatic Target Recognition (ATR) using a three-stage framework that leverages Low-Rank Adaptation (LoRA) and Supervised Contrastive Learning (SupCon). The method improves performance by 379% on a mission-level evaluation, with a significant reduction in training data required.