Lightweight Deep Learning Approaches for Lithological Mapping in Vegetated Terrains of the Vălioara Valley, Romania

Mapping lithology in areas with dense vegetation remains a major challenge for remote sensing, as plant cover tends to obscure the spectral signatures of underlying rock formations. This study tackles that issue by comparing the performance of three custom-built lightweight deep learning models in the mixed-vegetation terrain of the surroundings of the Vălioara Valley, Romania. We used time-series data from Sentinel-2 and elevation data from the SRTM, with preprocessing techniques such as the Principal Component Analysis (PCA) and the Forced Invariance Method (FIM) to reduce the spectral interference caused by vegetation. Predictions were made with a Multi-Layer Perceptron (MLP), a Convolutional Neural Network (CNN), and a Vision Transformer (ViT). In addition to measuring the classification accuracy, we assessed how the different models handled vegetation coverage. We also explored how vegetation density (NDVI) correlated with the classification results. Tests show that the Vision Transformer outperforms the other models by 6%, offering a stronger resilience to vegetation interference, while FIM doubled the model confidence in specific (locally rare) lithologies and decorrelated vegetation in multiple measures. These findings highlight both the potential of ViTs for remote sensing in complex environments and the importance of applying vegetation suppression techniques like FIM to improve geological interpretation from satellite data.