Jocelyn Chanussot is a Professor at Grenoble Institute of Technology, holding the AXA Chair of Remote Sensing. He is affiliated with GIPSA-Lab (Laboratoire de traitement du Signal et des Images) at Ense3 (École Nationale Supérieure de l'Énergie, l'Eau et l'Environnement) and maintains a connection with the Chinese Academy of Sciences through his AXA Chair position. His research focuses on advancing remote sensing technologies, particularly in hyperspectral imaging and artificial intelligence applications for environmental monitoring. Professor Chanussot specializes in hyperspectral imaging, which captures information across several hundred wavelengths, allowing for detailed characterization of physical properties in observed scenes. His work develops algorithms to extract meaningful information from complex remote sensing data, with applications spanning natural disaster monitoring, environmental observation, biodiversity assessment, and urban planning. He has pioneered approaches that leverage artificial intelligence, particularly deep learning techniques, to process and analyze large-scale remote sensing datasets. His scholarly output shows a clear trend toward integrating advanced AI techniques with remote sensing data. Recent publications demonstrate growing emphasis on transformer architectures, contrastive learning, generative models, and foundation models specifically adapted for hyperspectral data. His work increasingly addresses multimodal data fusion, anomaly detection, and real-time processing for applications in natural disaster response. Ranked among the 157 most cited French researchers in 2019 by Clarivate Analytics Professor Chanussot actively serves on numerous conference program committees, particularly for SPIE's Image and Signal Processing for Remote Sensing conferences. His research is supported by the AXA Research Fund through his AXA Chair in Remote Sensing, which focuses on developing algorithms for natural disaster monitoring and emergency response. He collaborates extensively with international institutions including UCLA and Stanford University on applications ranging from toxic gas detection to tropical forest biodiversity assessment. He leads research at GIPSA-Lab, focusing on developing advanced tools and algorithms for extracting information from complex remote sensing data. His team works on processing heterogeneous data including aerial photos, multispectral and hyperspectral images, and other environmental measurements to improve natural disaster prediction and response capabilities.
