Mercedes E. Paoletti

Universidad de Málaga, Universidad de Extremadura

Papers

Total Citations

146

H-Index

About

Dr. Mercedes E. Paoletti is a leading researcher in remote sensing and artificial intelligence, with a primary focus on hyperspectral image (HSI) classification and deep learning. Her most impactful contribution is the development of GhostNet for hyperspectral image classification (2021, 140 citations), a novel lightweight convolutional neural network architecture that dramatically reduces computational complexity while maintaining high accuracy. This work addresses a critical challenge in HSI processing—the massive data volumes generated by hundreds of contiguous spectral bands—making real-time Earth observation, medical imaging, and robotic vision more feasible. Dr. Paoletti’s research bridges the gap between spectral-spatial feature extraction and efficient model design, enabling practical deployment of AI in resource-constrained environments. Earlier in her career, she explored autonomous systems through work on a navigation agent for mobile manipulators (2015, 6 citations), demonstrating versatility in robotics. Her contributions have been widely recognized in the remote sensing community, with her GhostNet paper serving as a foundational reference for subsequent HSI classification studies. Dr. Paoletti continues to push boundaries in spectral intelligence, making her a key figure in advancing efficient, high-performance AI for Earth observation and beyond.