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Volume - 7 | Issue - 2 | june 2025

MG-ResViT: Dynamic Residual Learning with Contrastive Feature Optimization and PCA-Optimized Cross-Block Feature Fusion for Fine-Grained Mangrove Species Classification Open Access
Jasten Keneth D. Treceñe  , Arnel C. Fajardo  181
Pages: 420-446
Cite this article
Treceñe, Jasten Keneth D., and Arnel C. Fajardo. "MG-ResViT: Dynamic Residual Learning with Contrastive Feature Optimization and PCA-Optimized Cross-Block Feature Fusion for Fine-Grained Mangrove Species Classification." Journal of Innovative Image Processing 7, no. 2 (2025): 420-446
Published
24 June, 2025
Abstract

Mangrove conservation and monitoring are critically important for biodiversity. However, accurate classification remains challenging due to the morphological similarities among species. This paper proposes MG-ResViT, a novel deep learning framework that enhances mangrove species feature extraction for classification using a dynamic residual connection with spatially adaptive attention gates that capture discriminative local features, a hybrid loss that combines supervised contrastive learning and cross-entropy for optimizing feature space geometry, and PCA-optimized cross-block feature fusion for efficient multi-scale feature integration. The proposed model was evaluated using a ground-truth dataset of 3 mangrove species, composed of 1,000 images per species, which underwent preprocessing and data augmentation. Results revealed that the proposed MG-ResViT achieved an overall accuracy of 92.8% with only 6.2M parameters compared to other state-of-the-art models. Based on the results from the ablation studies conducted, the full MG-ResViT model provided excellent feature learning capability compared to the other model variants, with a high reduction in inter-class similarity (0.210) and improved in intra-class similarity (0.893). The silhouette scores also indicated that the full model has a well-defined and compact cluster (0.68) compared to other model variants such as the baseline EfficientNet-B0 + CE with 0.44, + SupCon only with 0.58, and + Dynamic Residuals only with 0.65. Moreover, the comparative analysis showed MG-ResViT (92.8%) outperformed ViT-Small (91.2%), ResNet-50 (89.3%), DenseNet-121 (90.0%), and EfficientNet-B0 (88.0%) in both accuracy and computational efficiency. Thus, the proposed MG-ResViT model has the potential for a more accurate fine-grained mangrove species classification, which is important for conservation and monitoring.

Keywords

Deep Learning Dynamic Residual Networks Ecological Conservation Fine-Grained Visual Recognition Mangrove Species Classification

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