
Abstract
In this study, we utilize a self-supervised ResNet50 model for classify land-cover imagery combining learning from both Sentinel-1 and Sentinel-2 images using the SEN12MS dataset. The model is further fine-tuned with the DFC2020 dataset, enriched by our addition of 336 new rubber data patches covering 75×90 km² area. The model achieved 98.1% accuracy in classifying rubber using only a 25% training data
split, and 74.9% accuracy in classifying forest








