Efficient Coastal Mangrove Species Recognition Using Multi-Scale Features Enhanced by Multi-Head Attention

Recognizing mangrove species is a challenging task in coastal wetland ecological monitoring due to the complex environment, high species similarity, and the inherent symmetry within the structural features of mangrove species. Many species coexist, exhibiting only subtle differences in leaf shape and color, which increases the risk of misclassification. Additionally, mangroves grow in intertidal environments with varying light conditions and surface reflections, further complicating feature extraction. Small species are particularly hard to distinguish in dense vegetation due to their symmetrical features that are difficult to differentiate at the pixel level. While hyperspectral imaging offers some advantages in species recognition, its high equipment costs and data acquisition complexity limit its practical application. To address these challenges, we propose MHAGFNet, a segmentation-based mangrove species recognition network. The network utilizes easily accessible RGB remote sensing images captured by drones, ensuring efficient data collection. MHAGFNet integrates a Multi-Scale Feature Fusion Module (MSFFM) and a Multi-Head Attention Guide Module (MHAGM), which enhance species recognition by improving feature capture across scales and integrating both global and local details. In this study, we also introduce MSIDBG, a dataset created using high-resolution UAV images from the Shankou Mangrove National Nature Reserve in Beihai, China. Extensive experiments demonstrate that MHAGFNet significantly improves accuracy and robustness in mangrove species recognition.