A Self-Supervised Hybrid Similarity Framework for Underwater Coral Species Classification

Few-shot learning has emerged as a crucial technique for coral species classification, addressing the challenge of limited labeled data in underwater environments. This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection. The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity, effectively capturing both feature magnitude and directional relationships. This approach achieves a notable accuracy of 71.8% under a 5-way 5-shot evaluation, outperforming state-of-the-art models such as Prototypical Networks, FEAT, and ESPT by up to 10%. Notably, the model demonstrates high precision in classifying Siderastreidae (87.52%) and Fungiidae (88.95%), underscoring its effectiveness in distinguishing subtle morphological differences. To further enhance performance, we incorporate a self-supervised learning mechanism based on contrastive learning, enabling the model to extract robust representations by leveraging local structural patterns in corals. This enhancement significantly improves classification accuracy, particularly for species with high intra-class variation, leading to an overall accuracy of 76.52% under a 5-way 10-shot evaluation. Additionally, the model exploits the repetitive structures inherent in corals, introducing a local feature aggregation strategy that refines classification through spatial information integration. Beyond its technical contributions, this study presents a scalable and efficient approach for automated coral reef monitoring, reducing annotation costs while maintaining high classification accuracy. By improving few-shot learning performance in underwater environments, our model enhances monitoring accuracy by up to 15% compared to traditional methods, offering a practical solution for large-scale coral conservation efforts.