Wetlands are essential ecosystems increasingly threatened by human activities and climate change. This study presents a method for classifying and monitoring wetland habitats in the Čiližská Radvaň protected area using RGB drone imagery and the Natural Numerical Network (NatNet), a mathematically based supervised deep learning approach. The primary aim was to evaluate the effectiveness of NatNet in identifying target habitat types and to assess the impact of ongoing revitalisation efforts. Habitat types were classified using RGB drone imagery and ground-truth training polygons representing the dominant vegetation communities in the Čiližská Radvaň wetland. The NatNet achieved a training classification success rate exceeding 97%, allowing the creation of relevancy maps that successfully identify spatial habitat distribution. Relevancy maps verified in the field achieved a classification accuracy of 0.88 and an F1 score of 0.90 across all habitats. Results showed observable shifts in habitat extent and structure after one year of restoration, confirming the method’s suitability for detecting ecological changes in wetland environments.