Applied Sciences, Год журнала: 2025, Номер 15(9), С. 4765 - 4765
Опубликована: Апрель 25, 2025
The increasing availability of satellite data and advances in machine learning (ML) have significantly enhanced land use image classification for environmental monitoring. However, the primary challenge using imagery lies presence cloud cover, variations resolution, seasonal changes, which impact accuracy reliability. This paper aims to improve assessment cover changes by proposing a hybrid ML, interpolation, vegetation indices-based approach. proposed approach was implemented random forest (RF) classifier, combined with interpolation indices, classify Sentinel-2 Baltic States. experimental results demonstrate that achieves an rate above 90%, effectively demonstrating its capacity distinguish between various types. We believe this study will inspire researchers practitioners further work towards applying ML algorithms offer valuable insights future tasks involving noise digitalization research.
Язык: Английский