A novel approach to coral species classification using deep learning and unsupervised feature extraction

This study presents a novel methodology to enhance coral species classification in underwater images by integrating Tri Convolutional Deep Autoencoders (TRI-CDAE) and Sparse Deep Autoencoders (SPDAE). TRI-CDAE employs three parallel CDAEs trained on distinct color spaces (RGB, HSV, LUV) to capture diverse features. These extracted features are fused and refined using SPDAE, promoting sparsity and enhancing discriminative power. The refined features are then classified using a softmax classifier. Evaluation on four coral image datasets shows exceptional performance, with recall (96.5%), F1 score (97.4%), precision (97.2%), accuracy (98.5%), Cohen's J (0.959), Jaccard Index (0.971), Cohen's Kappa (0.961), and Matthews Correlation Coefficient (0.982).