Jonathan Cheung-Wai Chan is an expert in hyperspectral remote sensing and its applications using novel machine learning algorithms and deep learning approaches.

Shortly after obtaining his PhD from the University of Hong Kong (1999), he worked as a research scientist at the Geography Department, at University of Maryland, College Park, the United States. He was involved in various NASA projects in relation to global mapping using machine learning algorithms (C5.0, Bagging, Boosting). In 2001-2004, he was a post-doc researcher at InterUniversity MicroElectronics Research Center (IMEC) at Leuven, stationed at the Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB). He worked on European projects using remote sensing methods for humanitarian demining (Thermal Infrared sensor, UAV HD multi-spectral analysis). From 2005 to 2011, he was a Doctor Assistant at the Geography Department VUB where he led research projects for exploitation of airborne and spaceborne hyperspectral remote sensing in novel applications such as NATURA 2000 ecotope mapping for biodiversity, and impervious surface estimation for hydrological modeling in urban area.

From 2013-2015, he was a Marie Curie Fellow under the stream of Marie Curie Industry-Academia Partnerships and Pathways (IAPP) program, at Fondazione Edmund Mach, Trentino, Italy. Research in Hyperspectral/Lidar Fusion of for forestry inventory.

He serves regularly in the Technical and Scientific Committees of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) and has chaired several special sessions in IGARSS on Spatial Superresolution and Subpixel Classifications of hyperspectral data.

Enhancement of satellite images. Spatial and spectral superresolutions using Deep Learning, Tensor Theory and Sparse Theory. Application of advanced Earth Observation tools and techniques for greener environment, e.g. ocean plastics, biodiversity, climate mitigation.