Satellite Image Analysis Using Modified EfficientNet

Abstract

Climate change is reshaping the Earth’s surface through vegetation loss, desertification, and water depletion, necessitating efficient automated analysis of satellite imagery. This work evaluates three lightweight EfficientNet-based architectures for satellite image classification using four classes from the RSI-CB256 dataset and ten classes from the EUROSAT dataset. The models include EfficientNet-Lite with a Squeeze-and-Excitation (SE) head, EfficientNet-B0 with a pointwise head, and an SE-enhanced EfficientNet-B0, all trained under identical settings with parameter counts between 3.37M and 4M. Experimental results show that
the SE-enhanced EfficientNet-B0 achieves the highest accuracy, reaching 99.83% on RSI-CB256 and 95.10% on EUROSAT, while maintaining computational efficiency. These findings highlight the effectiveness of SE-augmented EfficientNet architectures for accurate and scalable AI-driven climate monitoring