<em>Posidonia oceanica</em> is one of the most important seagrass species in the Mediterranean Sea, providing essential ecosystem services such as carbon sequestration, coastal protection and acting as a habitat and nursery ground for numerous marine species. These meadows have experienced significant decline in recent decades due to increasing anthropogenic pressures and environmental changes. Accurate and efficient mapping techniques are therefore essential for monitoring their spatial distribution and supporting conservation efforts. This study investigates the potential of very high-resolution Neo-Pl&eacute;iades satellite imagery for mapping <em>P. oceanica</em> meadows along the northeastern coast of Sardinia (Italy). Two satellite acquisitions from 2021 and 2022 were orthorectified in PCI Catalyst (v.2023.0.0) using a Rational Polynomial Coefficient (RPC) model. Subsequently, a water column correction based on the Lyzenga depth-invariant index was applied to reduce depth-related spectral variability. The images were then classified using an object-based image analysis approach implemented in eCognition Developer (v.10.5), comparing three supervised algorithms: Nearest Neighbor (NN), Support Vector Machines (SVM), and Random Tree (RT). Accuracy assessment based on confusion matrices showed high classification performance, with overall accuracies up to 0.97 and Kappa values up to 0.96. Additional spatial validation using manually delineated reference areas confirmed classification reliability, although slightly lower agreement values were observed compared to confusion matrix estimates. The results highlight the strong potential of integrating high-resolution satellite imagery, water column correction, and object-based classification for mapping and monitoring <em>P. oceanica</em> habitats.