LAUSR

Abstract In recent years, two types of macroalgae, namely, Ulva prolifera and Sargassum horneri, have appeared occasionally together in the Yellow Sea and the East China Sea. Remote sensing enables timely and cost-effective observation of macroalgae across large areas. In the available studies, the recognition and classification of the two macroalgae are primarily based on spectral difference analysis. In this study, the spectral features, indices and textural feature parameters of the macroalgae targets were extracted and a preliminary multi-feature dataset was constructed based on Sentinel-2 images. Feature selection was performed using SHAP-based importance analysis and Bhattacharyya distance. From this, a multi-feature dataset was created and used as an input to a deep semantic segmentation network of improved ResUnet. The experiments of intelligent recognition and classification of U. prolifera and S. horneri were carried out using the proposed multi-feature-based ResUnet algorithm, with specific F1-scores of 96.7% and 96.8%, respectively. The proposed multi-feature-based ResUnet algorithm can obtain efficient and high-accuracy results for the recognition and classification of marine floating macroalgae. It achieves accurate remote sensing monitoring of the two types of marine floating macroalgae and has significant theoretical research significance and practical application value.