Enhancing Land Cover Classification: Fuzzy Similarity Approach Versus Random Forest

This study presents a comparative analysis of two advanced classification techniques applied to Landsat 8 and Sentinel-2 imagery. The first technique is based on the combined use of Tversky’s fuzzy similarity and Mamdani-type fuzzy inference, specifically designed to handle transition zones—areas characterized by gradual shifts in land cover, such as from vegetation to suburban environments. The second approach is based on the Random Forest algorithm. After performing the ranking of spectral, textural, and geometric features using the fuzzy approach, a fuzzy system based on Tversky’s fuzzy similarity was developed. This system enables a more adaptive and nuanced classification of different land cover classes, including water bodies, forests, and cultivated areas. The results indicate that the proposed fuzzy approach slightly outperforms the Random Forest method in handling mixed land cover regions and reducing classification uncertainties, achieving overall accuracies of 98.5% for Sentinel-2 and 96.7% for Landsat 8.