Assist. Prof. Dr. Rafael Lemos Paes | Remote Sensing | Research Excellence Award
Assist. Prof. Dr. Rafael Lemos Paes | Remote Sensing | Brazilian Air Force Academy | Brazil
Assist. Prof. Dr. Rafael Lemos Paes is a Remote Sensing Scientist specializing in Synthetic Aperture Radar and artificial intelligence applications for maritime and aerospace domains, with a career that integrates advanced academic research, operational defense expertise, and strategic technological advisory roles. Rafael Lemos Paes holds a PhD and a Master’s degree in Remote Sensing from the National Institute for Space Research in Brazil and a Bachelor’s degree in Aeronautical Sciences from the Brazilian Air Force Academy, reflecting a strong interdisciplinary foundation spanning Earth observation, computational intelligence, and aeronautical operations. During his doctoral training, Rafael Lemos Paes served as a visiting researcher at the Università degli Studi di Napoli “Parthenope” in Italy, where he conducted internationally supervised research that strengthened his expertise in spaceborne and airborne radar systems. Professionally, Rafael Lemos Paes currently serves at the Brazilian Air Force General Staff, where he provides high-level advisory support in C4ISR, advanced technologies, research and development, strategic innovation initiatives, and long-term defense and aerospace programs, bridging scientific research with national security and aerospace decision-making. His research interests are centered on Synthetic Aperture Radar data analysis, compact polarimetric SAR in hybrid modes, deep learning and automatic pattern recognition, maritime surveillance, and the extraction of actionable intelligence from large-scale remote sensing datasets. Rafael Lemos Paes has made notable contributions to maritime target detection over ocean surfaces, including ship detection, oil spill monitoring, and small vessel identification in complex environments such as Amazonian river systems, as well as aircraft accident analysis and shipwreck debris detection using SAR imagery. His research skills include SAR signal processing, polarimetric analysis, Big Data analytics, machine learning and deep learning model development, computational intelligence techniques, geospatial data integration, and the operational exploitation of airborne and spaceborne sensor data for defense and civil applications.
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Featured Publications
Ship Detection Using TerraSAR-X Images in the Campos Basin (Brazil)
– IEEE Geoscience and Remote Sensing Letters, 2010 · 80 citations
Single-Look Complex COSMO-SkyMed SAR Data to Observe Metallic Targets at Sea
– IEEE JSTARS, 2012 · 47 citations
Oil Rig Recognition Using Convolutional Neural Networks on Sentinel-1 SAR Images
– IEEE Geoscience and Remote Sensing Letters, 2019 · 36 citations
On the Capability of Hybrid-Polarity Features to Observe Metallic Targets at Sea
– IEEE Journal of Oceanic Engineering, 2015 · 24 citations
Marine Environmental Impact on CFAR Ship Detection as Measured by Wave Age in SAR Images
– Remote Sensing, 2023 · 20 citations