Prof. Melba Crawford
Melba Crawford is a the Nancy Uridil and Francis Bossu Professor of Civil Engineering at Purdue, where she is also a professor in the Schools of Electrical and Computer Engineering and the Department of Agronomy. Previously, she was an Engineering Foundation Endowed Professor in Mechanical Engineering at the University of Texas at Austin, where she founded an interdisciplinary research and applications development program in space-based and airborne remote sensing. Dr. Crawford’s research focuses on development of machine learning based algorithms for classification and prediction, and applications of these methods to hyperspectral and LIDAR remotely sensed data. She has authored more than 200 publications in scientific journals, conference proceedings, book chapters, and technical reports. Dr. Crawford is a Fellow and Life Member of the IEEE, Past President of the IEEE Geoscience and Remote Sensing Society (GRSS), an IEEE GRSS Distinguished Lecturer, an Associate Editor of the IEEE Transactions on Geoscience and Remote Sensing, and past Treasurer of the IEEE Technical Activities Board. She received the GRSS outstanding Service Award in 2020 and the IEEE GRSS David Landgrebe Research Award in 2021.
Prof. Saurabh Prasad
Saurabh Prasad (Senior Member, IEEE) received his Ph.D. in electrical and computer engineering from Mississippi State University, MS, USA, in 2008. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, where he directs the Machine Learning and Signal Processing Laboratory. His lab focuses on advancing state-of- the-art in machine learning and signal processing with applications to remote sensing and biomedicine. His work has been recognized by two student research awards during his Ph.D. study, a best student paper award at the 2008 IGARSS conference, top-10% papers at IEEE-ICIP conference, a NASA New Investigator (Early Career) award in 2014, and the junior faculty research excellence award at the University of Houston in 2017. He currently serve as an associate editor for the IEEE Signal Processing Letters and the IEEE Transactions on Geoscience and Remote Sensing.
Dr. Caleb Robinson
Caleb is a Data Scientist in the Microsoft AI for Good Research Lab. His work focuses on tackling large scale problems at the intersection of remote sensing and machine learning/computer vision. Some of the projects he works on include: estimating land cover from high-resolution satellite imagery across the continent, detecting concentrated animal feeding operations (CAFOs) from aerial imagery, and estimating human population density from satellite imagery. Caleb is interested in research topics that facilitate using remotely sensed imagery more effectively in these types of problems in computational sustainability. For example: self-supervised methods for training deep learning models with large amounts of unlabeled satellite imagery, human-in-the-loop methods for creating and validating modeled layers, and domain adaptation methods for developing models that can generalize over space and time.
DeepMachine Learning for Spectral Unmixing
Dr. Behnood Rasti
DeepMachine Learning for Spectral Unmixing
Behnood Rasti (Senior Member, IEEE) received the B.Sc. and M.Sc. degrees in electronics and electrical engineering from the Electrical Engineering Department, University of Guilan, Rasht, Iran, in 2006 and 2009, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Iceland, Reykjavik, Iceland, in 2014. From 2015 to 2016, he was a Post-Doctoral Researcher with the Electrical and Computer Engineering Department, University of Iceland. From 2016 to 2019, he was a Lecturer with the Center of Engineering Technology and Applied Sciences, Department of Electrical and Computer Engineering, University of Iceland. He was a Humboldt Research Fellow in 2020 and 2021.
He is currently a Principal Research Associate with Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Freiberg, Germany. His research interests include signal and image processing, machine/deep learning, remote sensing, and artificial intelligence. Dr. Rasti was the Valedictorian as an M.Sc. student in 2009. He won the Doctoral Grant of The University of Iceland Research Fund “The Eimskip University Fund” and the “Alexander von Humboldt Research Fellowship Grant” in 2013 and 2019, respectively. He also serves as an Associate Editor for the IEEE Geoscience and Remote Sensing Letters (GRSL).
Dr. Matteo Ciotola
Matteo Ciotola received the B.Sc. and M.Sc. (cum laude) degrees from the University of Naples Federico II, Italy, in 2018, and 2020, respectively, both in automation engineering. He is currently a PhD fellow at the University of Naples Federico II as member of the GRIP research team. In 2020, he held a traineeship with the “Centre de coopération internationale en recherche agronomique pour le développement” (CIRAD), Montpellier, France, carrying out his research activity at the “Maison de la télédétection”. His main research interests focus on data fusion of remotely sensed images, in particular super-resolution and pansharpening, through deep learning algorithms.
Prof. Giuseppe Scarpa
Giuseppe Scarpa is Associate Professor of Telecommunications at the University of Naples Federico II. In 2005 he has been research fellow of the Pattern Recognition Department, UTIA Institute of the Czech Academy of Sciences, Prague. In 2006 he has been research fellow at INRIA in Sophia Antipolis (F). His research activity has dealt with image segmentation, texture modeling and classiﬁcation, object detection, pansharpening, feature extraction, data fusion, SAR despeckling, image coregistration. In the last years, his research work has mostly concerned deep learning-based data fusion techniques with application in the remote sensing domain. Prof. Scarpa has been Senior Area Editor for IEEE Signal Processing Letters. He has also served as Guest Editor for several special issues of the MDPI Remote Sensing journal.
Learning with Zero Few Labels
Dr. Sudipan Saha
Learning with Zero Few Labels
Sudipan Saha received the PhD degree in information and communication technologies from the University of Trento, Trento, Italy, and Fondazione Bruno Kessler, Trento, Italy in 2020. Previously, he obtained the M.Tech. degree in electrical engineering from IIT Bombay, Mumbai, India, in 2014. He is currently a postdoctoral researcher at Technical University of Munich (TUM), Munich, Germany since 2020. Previously, he worked as an Engineer with TSMC Limited, Hsinchu, Taiwan, from 2015 to 2016. He is the recipient of Fondazione Bruno Kessler Best Student Award 2020. His research interests are related to multitemporal and multi-sensor remote sensing image analysis, self-supervised learning, image segmentation, and uncertainty quantification. Dr. Saha is a Reviewer for several international journals. He served as a guest editor at Remote Sensing (MDPI) special issue on “Advanced Artificial Intelligence for Remote Sensing: Methodology and Application.
Dr. Angelica I. Aviles-Rivero
Learning with Zero Few Labels
Angelica I. Aviles-Rivero is currently a Senior Research Associate with the Department of Applied Mathematics & Theoretical Physics (DAMTP), University of Cambridge, Cambridge, U.K. Her research lies at the intersection of computational mathematics, computer vision, and machine learning for applications to large-scale real-world problems. Her central research is to develop new data-driven algorithmic techniques that allow computers to gain high-level understanding from vast amounts of data, with the aim of aiding the decisions of users from multiple disciplines. This line of research has allowed her to gain expertise with a wide range of various data types, including medical imaging, computational photography, computer graphics, and remote sensing to new a few. She is currently co-organising MIUA/WiMIUA and GeoMedIA 2022. She is an elected officer of SIAM SIAG/IS.
Dr. Lichao Mou
Learning with Zero Few Labels
Lichao Mou received the Bachelor’s degree in automation from the Xi’an University of Posts and Telecommunications, Xi’an, China, in 2012, the Master’s degree in signal and information processing from the University of Chinese Academy of Sciences (UCAS), China, in 2015, and the Dr.-Ing. degree from the Technical University of Munich (TUM), Munich, Germany, in 2020. He is currently a Guest Professor with the Munich AI Future Laboratory “Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (AI4EO), TUM; and the Head of the Visual Learning and Reasoning Team, Department “EO Data Science,” Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Wessling, Germany. Since 2019, he has been a Research Scientist with DLR-IMF and an AI Consultant with the Helmholtz Artificial Intelligence Cooperation Unit (HAICU). He was the recipient of the first place in the 2016 IEEE GRSS Data Fusion Contest and finalists for the Best Student Paper Award at the 2017 Joint Urban Remote Sensing Event and 2019 Joint Urban Remote Sensing Event.
Prof. Carola-Bibiane Schönlieb
Learning with Zero Few Labels
Carola-Bibiane Schönlieb is Professor of Applied Mathematics at the University of Cambridge. There, she is head of the Cambridge Image Analysis group and co-Director of the EPSRC Cambridge Mathematics of Information in Healthcare Hub. Her current research interests focus on variational methods, partial differential equations and machine learning for image analysis, image processing and inverse imaging problems. Her research has been acknowledged by scientific prizes, among them the LMS Whitehead Prize 2016, the Philip Leverhulme Prize in 2017, the Calderon Prize 2019, a Royal Society Wolfson fellowship in 2020. Carola graduated from the Institute for Mathematics, University of Salzburg (Austria) in 2004. From 2004 to 2005 she held a teaching position in Salzburg. She received her PhD degree from the University of Cambridge (UK) in 2009. After one year of postdoctoral activity at the University of Göttingen (Germany), she became a Lecturer at Cambridge in 2010, promoted to Reader in 2015 and promoted to Professor in 2018.
Prof. Xiao Xiang Zhu
Learning with Zero Few Labels
Xiao Xiang Zhu received the Master (M.Sc.) degree, her doctor of engineering (Dr.-Ing.) degree and her “Habilitation” in the field of signal processing from Technical University of Munich (TUM), Munich, Germany, in 2008, 2011 and 2013, respectively. She is currently the Professor for Data Science in Earth Observation at Technical University of Munich (TUM) and the Head of the Department “EO Data Science” at the Remote Sensing Technology Institute, German Aerospace Center (DLR). Since 2019, Zhu is a co-coordinator of the Munich Data Science Research School (www.mu-ds.de). Since May 2020, she is the director of the international future AI lab “AI4EO — Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond”, Munich, Germany. She serves in the scientific advisory board in several research organizations, among others the German Research Center for Geosciences (GFZ) and Potsdam Institute for Climate Impact Research (PIK). She is an associate Editor of IEEE Transactions on Geoscience and Remote Sensing and serves as the area editor responsible for special issues of IEEE Signal Processing Magazine. She is a Fellow of IEEE.
Prof. Avik Bhattacharya
Avik Bhattacharya received the integrated M.Sc. degree in Mathematics from the Indian Institute of Technology, Kharagpur, India, in 2000 and the Ph.D. degree in remote sensing image processing and analysis from Télécom ParisTech, Paris, France, and the Ariana Research Group, Institut National de Recherche en Informatique et en Automatique (INRIA), Sophia Antipolis, Nice, France, in 2007. He is a Professor at the Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay (CSRE, IITB), Mumbai, India. Before joining IITB, he was a Canadian Government Research Fellow at the Canadian Centre for Remote Sensing (CCRS) in Ottawa, ON, Canada. He received the Natural Sciences and Engineering Research Council of Canada’s prestigious visiting scientist fellowship at the Canadian national laboratories from 2008 to 2011. His current research interests include Imaging Radar polarimetry, statistical analysis of polarimetric Synthetic Aperture Radar (SAR) images, applications of Radar Remote Sensing in Agriculture, Cryosphere, Urban and Planetary studies. Dr. Bhattacharya is the Editor-in-Chief of IEEE Geoscience and Remote Sensing Letters (GRSL). He is an Associate Editor of the Journal of Remote Sensing, A Science Partner Journal. He served as an Associate Editor of IEEE GRSL. In 2017, he established the IEEE GRSS chapter of the Bombay section. He was a guest editor of the special issue on Applied Earth Observations and Remote Sensing in India in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 2017. He was a guest editor of the Special Stream in IEEE GRSL on Advanced Statistical Techniques in SAR Image Processing and Analysis, 2018. He has been the Publication Chair of several IEEE Geoscience and Remote Sensing Conferences. He is the keynote and guest speaker for several conferences and workshops. He is the scientific committee member of the European Space Agency’s POLINSAR workshop. He has been a member of the International Steering Committee and the International Advisory Committee Member of the Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) 2021 and BIGSARDATA 2021. He is the Scientific Advisor on Earth Observation at Cropin Technology Solutions Private Limited, India.
Prof. Alejandro Frery
Alejandro C. Frery has Ph.D. degree Applied Computing from the Instituto Nacional de Pesquisas Espaciais (INPE, São José dos Campos, Brazil, 1993). He is currently Professor of Statistics and Data Science with Te Herenga Waka – The Victoria University of Wellington, New Zealand. After serving as Associate Editor for more than five years, Prof. Frery was the Editor-in-Chief of the IEEE Geoscience and Remote Sensing Letters for the period 2014–2018. He was IEEE Geoscience and Remote Sensing Society (GRSS) Distinguished Lecturer during 2015–2019. Since 2019 he serves as AdCom (Advisory Committee) member for this Society, in charge of Future Publications, and Plagiarism. His research interests are data visualization, statistical computing, and stochastic modeling, with applications in signal and image processing, and networks.
Dr. Dipankar Mandal
Dr. Dipankar Mandal received the B.Tech. degree in agricultural engineering from Bidhan Chandra Krishi Viswavidyalaya, India, in 2015, and the M.Tech and Ph.D. dual degrees in geoinformatics and natural resources engineering from the Indian Institute of Technology Bombay, Mumbai, India, in 2020. He was the recipient of the IEEE Geoscience and Remote Sensing Society India Best Ph.D. thesis award, in 2020, and the ‘Excellence in Ph.D. Research’ from Indian Institute of Technology Bombay, in 2021, for his doctoral work entitled “Retrieval of Biophysical Parameters for Agricultural Crops using Polarimetric SAR Data” and outstanding contribution toward improvement of diverse techniques for agricultural applications utilizing synthetic aperture radar remote sensing data. Currently, Dr. Mandal is a Postdoctoral Fellow at Department of Agronomy, Kansas State University, USA. His research interest embraces precision agriculture and remote sensing including SAR polarimetry and multi-spectral data for crop classification, vegetation biophysical parameter estimation, deriving radar vegetation indices, and machine learning applications.
Dr. Shashi Kumar
Dr. Shashi Kumar received the M.Sc. degree in Physics from Patna University, Patna, India, in 2002, the M.Sc. degree in Geoinformatics under the joint education program of Indian Institute of Remote Sensing (IIRS), Dehradun, India, and International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands, in 2009, and the Ph.D. degree from Indian Institute of Technology (IIT), Roorkee, India, in 2019. Since 2009, he has been working as Scientist with the Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO), Dehradun, India. Dr. Shashi Kumar is a dedicated researcher contributing to capacity building through education and research in the field of advanced Synthetic Aperture Radar (SAR) remote sensing that includes Polarimetric SAR (PolSAR), Polarimetric SAR Interferometry (PolInSAR), and Polarimetric Tomographic SAR (PolTomSAR), its data processing techniques, and applications. Over the past 12 years, Dr. Shashi Kumar has shown intellectual prowess in cutting-edge research works on Polarimetric SAR Remote Sensing (PolSAR), and he has published 50 journal articles, 108 conference papers, 5 newsletters, 12 Book Chapters & Lecture Notes, and 13 project reports. He has shown excellent collaborative skills by doing collaborative research work with scientists and professors from various universities and institutions. He has edited two journal issues on special topics on ‘Advances in Spaceborne SAR Remote Sensing for Characterization of Natural and Manmade Features-1, &II’ for the COSPAR Journal, Advances in Space Research (ASR). Currently, Dr. Kumar is a guest editor for an special issue of the Advances in Space Research (ASR) for a topic on ‘Synergistic Use of Remote Sensing Data and In-Situ Investigations to Reveal the Hidden Secrets of the Moon’, and 1 issue of the AGU Wiley Earth and Space Science for a special topic on ‘Synthetic Aperture Radar Remote Sensing for Characterization of Land Use and Land Cover’. Dr. Shashi Kumar has worked as a member of the SAR Task Group to develop SAR protocols under the partnership of the Government of India (GoI) and the United States Agency for International Development (USAID) Forest-PLUS program. He has also performed his duties as a committee member for Commonwealth Scholarship 2016 at the Department of Higher Education, Ministry of Human Research Development. He is a science team member for NASA-ISRO Synthetic Aperture Radar (NISAR) mission for the Science Products, Calibration, and Tools Development team and a science team member of Chandrayaan-2 mission’s Working Group 3 for Lunar Poles and Microwave Remote Sensing. One of his significant contributions is the development of a methodological framework for Polarimetric Calibration (PolCal) of Airborne and Spaceborne fully polarimetric and hybrid/compact-pol SAR data to minimize distortions for scattering-based characterization of manmade and natural objects. In recognition of outstanding contributions toward “Polarimetric Calibration of SAR data and development of PolSAR” and “PolInSAR modelling approaches for scattering-based characterization of manmade and natural objects”, the Indian Society of Remote Sensing (ISRS) conferred the Indian National Geospatial Award 2021 to Dr Shashi Kumar.
Prof. Sylvain Lobry
Sylvain Lobry is an associate professor at the Université Paris Cité, at the Laboratoire d’Informatique de Paris Descartes (LIPADE) and at the UFR de Mathématiques et Informatique since 2020. Previously, he was a postdoctoral researcher at the University of Wageningen, the Netherlands, in the Geo-Information and Remote Sensing Laboratory. He received his PhD in image processing at Télécom Paris in 2017 and the best PhD award from the Futures et Ruptures program. His research interests lie in the areas of methodological developments in image processing with applications in particular to remote sensing imagery. This includes high resolution optical image processing using deep learning techniques; image-text interactions; change detection; semantic segmentation and regularization of SAR image time series using Markovian models. During his PhD, he worked (in collaboration with CNES) on the SWOT mission, dedicated to the study of oceans and surface water bodies.
Since 2021, he is the vice chair of the IAPR Technical Committee 7 (Remote Sensing and Mapping) and co-organizes the workshop on pattern recognition in remote sensing at ICPR 2022. He is a reviewer for several international journals (including IEEE TGRS, JSTARS and GRSL) and international conferences (including IGARSS, CVPR, ECCV, ICPR) in computer vision and remote sensing.
Dr. Michele Ronco
Michele Ronco studied physics at the University of Rome “La Sapienza”, where he obtained his PhD in 2019 with a thesis on the phenomenology of quantum gravity approaches in astrophysical observations. During the PhD he was a visitor scientist in several research institutions, among which Penn State University, Fudan University, IEM-CSIC, and University of Valencia. He then joined the HESS telescope group at the University Pierre and Marie Curie in Paris where he worked on tests and simulations of Lorentz invariance violation models in the propagation of high-energy gamma rays. After that, he worked as data scientist in the industry both in the insurance and in the remote sensing sectors, and applied CNN models for the classification and segmentation of Sentinel-2 images. He is currently a postdoc at the Image Processing Laboratory of the University of Valencia with a focus on explainable machine learning methods for a variety of earth science problems, ranging from wildfire forecasting to human movements induced by weather hazards.