DMDNet: Decoupled Multimodal Detection Network for Fine-Grained Ulva Prolifera Segmentation

Ulva prolifera detection is of great significance for marine ecological monitoring and green tide disaster prevention and control. Current single-modal detection methods have inherent limitations. Optical RGB imagery is highly vulnerable to cloud occlusion, while synthetic aperture radar (SAR) data is contaminated by severe speckle noise. Furthermore, existing multimodal detection algorithms struggle to address the prominent multimodal feature heterogeneity between optical and SAR remote sensing data. To overcome these challenges, we develop a decoupled multimodal detection network (DMDNet) for fine-grained Ulva prolifera segmentation. First, a dual-branch feature extraction module with parallel alignment encoding is constructed to adapt to heterogeneous inputs of two modalities. Second, a dedicated convolutional layer unifies the dimensions of the two-modality feature streams. The processed features are subsequently passed to the encoder–decoder module, where the network exploits available features from both optical and SAR modalities for segmentation. Third, a multimodal comprehensive loss function is designed to mitigate the segmentation accuracy degradation caused by class imbalance and blurry target boundaries. In addition, a multimodal joint training strategy is adopted to train the model with optical and SAR samples simultaneously in each iteration. Equipped with a shared encoder and independent task-specific decoder heads, DMDNet accepts either a single optical image or a single SAR image as input and generates stable and reliable segmentation results. Comprehensive experiments are conducted on FIO-EP and CODC datasets, which demonstrate that DMDNet outperforms other baseline models.