Antonio Plaza

Papers

Total Citations

283

H-Index

About

Antonio Plaza is a leading figure in remote sensing and hyperspectral imaging, renowned for pioneering deep learning techniques that tackle the immense data challenges of Earth observation. His work bridges computer vision and environmental science, with major contributions in image segmentation and hyperspectral data analysis. Plaza’s highly cited survey on deep learning for image segmentation (143 citations) provides a comprehensive roadmap for scene understanding, medical imaging, and robotic perception. He further advanced the field with GhostNet for hyperspectral image classification (140 citations), a lightweight architecture that efficiently processes hundreds of narrow spectral bands—critical for applications from planetary exploration to quality control. His research addresses the core paradox of hyperspectral imaging: the richness of data that is both a solution and a computational burden. Plaza’s impact is evident in his sustained citation record and his role in shaping how deep learning models are adapted for high-dimensional remote sensing data. His work continues to inspire students and researchers seeking to harness AI for environmental monitoring, defense, and precision agriculture.