TCCFNet: a semantic segmentation method for mangrove remote sensing images based on two-channel cross-fusion networks

Abstract

Mangrove ecosystems play a crucial role in coastal environments. However, due to the complexity of mangrove distribution and the similarity among different categories in remote sensing images, traditional image segmentation methods struggle to accurately identify mangrove regions. Deep learning techniques, particularly those based on CNNs and Transformers, have demonstrated significant progress in remote sensing image analysis. This study proposes TCCFNet (Two-Channel Cross-Fusion Network) to enhance the accuracy and robustness of mangrove remote sensing image semantic segmentation. This study introduces a dual-backbone network architecture that combines ResNet for fine-grained local feature extraction and Swin Transformer for global context modeling. ResNet improves the identification of small targets, while Swin Transformer enhances the segmentation of large-scale features. Additionally, a Cross Integration Module (CIM) is incorporated to strengthen multi-scale feature fusion and enhance adaptability to complex scenarios. The dataset consists of 230 high-resolution remote sensing images, with 80% used for training and 20% for validation. The experimental setup employs the Adam optimizer with an initial learning rate of 0.0001 and a total of 450 training iterations, using cross-entropy loss for optimization. Experimental results demonstrate that TCCFNet outperforms existing methods in mangrove remote sensing image segmentation. Compared with state-of-the-art models such as MSFANet and DC-Swin, TCCFNet achieves superior performance with a Mean Intersection over Union (MIoU) of 88.34%, Pixel Accuracy (PA) of 97.35%, and F1-score of 93.55%. Particularly, the segmentation accuracy for mangrove categories reaches 99.04%. Furthermore, TCCFNet excels in distinguishing similar categories, handling complex backgrounds, and improving boundary detection. TCCFNet demonstrates outstanding performance in mangrove remote sensing image segmentation, primarily due to its dual-backbone design and CIM module. However, the model still has limitations in computational efficiency and small-target recognition. Future research could focus on developing lightweight Transformer architectures, optimizing data augmentation strategies, and expanding the dataset to diverse remote sensing scenarios to further enhance generalization capabilities. This study presents a novel mangrove remote sensing image segmentation approach—TCCFNet. By integrating ResNet and Swin Transformer with the Cross Integration Module (CIM), the model significantly improves segmentation accuracy, particularly in distinguishing complex categories and large-scale targets. TCCFNet serves as a valuable tool for mangrove remote sensing monitoring, providing more precise data support for ecological conservation efforts.