TECHNIQUES FOR CLASSIFYING SEABED SEDIMENTS USING SATELLITE IMAGES: A SYSTEMATIC REVIEW OF THE LITERATURELO (REMUNOM, 2026)
This systematic literature review aims to analyze the main techniques for classifying seabed sediments from satellite imagery. The research was conducted in the IEEE Xplore, SciELO, PubMed, and Google Scholar databases, encompassing publications between 2015 and 2025. The results highlight significant advances in marine remote sensing methodologies, particularly the integration of optical and altimetric sensors (Sentinel-2, Landsat 8, and ICESat-2) and the combined use of empirical models, spectral regressions, and supervised algorithms such as Random Forest, SVM, and convolutional neural networks (CNNs). Although studies focused on bathymetry predominate, a gradual increase in research dedicated to optical sediment classification was observed. Deep learning-based approaches showed promise, although their performance is still limited by water turbidity and the scarcity of labeled data. It is concluded that multisensory fusion, associated with the integration between empirical models and deep learning techniques, represents the main future trend for improving accuracy, reducing dependence on in situ surveys, and expanding the use of remote sensing in the environmental monitoring and management of coastal zones.