An intelligent identification and classification method for tidal flat vegetation integrating Relief F and CART

Tidal flat vegetation constitutes a crucial component of coastal wetland ecosystems, and its spatial distribution and biological information are of great significance for regional ecological security and sustainable development. To address the challenges of low recognition accuracy, high feature dimensionality, and severe data redundancy in existing remote sensing methods under complex tidal flat environments, this study proposes a ReliefF-based Nearest Neighbor feature optimization method (RFNN). The proposed approach evaluates feature importance to select key variables and achieve dimensionality reduction, thereby effectively eliminating redundant information. Combined with the Classification and Regression Tree (CART) algorithm, an efficient vegetation classification model is established. The results demonstrate that the RFNN method significantly enhances classification efficiency and recognition accuracy in high-resolution tidal flat imagery, particularly in small-scale and high-precision vegetation monitoring. This method not only provides technical support for refined identification and dynamic monitoring of tidal flat vegetation but also offers a new perspective and methodological reference for vegetation classification in complex environments.