Josiane Zerubia professzor, az INRIA (Sophia Antipolis) laborvezetője "Hierarchical Probabilistic Graphical Models and Neural Networks for Remote Sensing Image Classification: Application to Natural Disasters and Urban Studies" címmel tart előadást a SZTAKI-ban.
Az angol nyelvű előadás 14:30-kor kezdődik a SZTAKI Kende utcai épületének alagsori nagytermében. Zerubia professzor előadásának eredeti nyelven írt absztraktja alább olvasható.
The task of monitoring the Earth´s surface plays an important role in the framework of the protection from environmental disasters such as flooding, landslide, or earthquake, and in the field of urban land cover. Thanks to the substantial amount and variety of information available from the current space missions, models for multimodal data, typically multiview, multiscale, and multiresolution methods are becoming more and more important to face the requirements of remote sensing applications. In this framework, the challenge is to develop accurate and time-efficient classification methods, flexible enough to exploit information contained in multimodal data. In the proposed approaches, multimodal fusion is addressed by supervised classification methods based on hierarchical Markov models with a quadtree topology. These models have also been combined with deep neural networks. The marginal posterior mode (MPM) criterion is used for inference in the proposed framework. The developed methods have been experimentally validated with datasets containing very-high-resolution multispectral, panchromatic, and radar satellite images, or aerial images. The experimental results suggest that the methods are able to provide accurate classification maps from input heterogeneous imagery. The comparison with the state of the art techniques shows the effectiveness of the proposed approaches.
Josiane Zerubia has been a permanent research scientist at INRIA since 1989 and director of research since July 1995 (DR 1st class since 2002).
She received the MSc degree from the Department of Electrical Engineering at ENSIEG, Grenoble, France in 1981, the Doctor of Engineering degree, her PhD and her `Habilitation´, in 1986, 1988, and 1994 respectively, all from the University of Nice, France.
She was head of the PASTIS remote sensing laboratory (INRIA Sophia-Antipolis) from mid-1995 to 1997 and of the ARIANA research group (INRIA/CNRS/University of Nice), which worked on inverse problems in remote sensing and biological imaging, from 1998 to 2011. From 2012 to 2016, she was head of AYIN research group (INRIA-SAM) dedicated to models of spatio-temporal structure for high-resolution image processing with a focus on remote sensing and skincare imaging. She is head of AYANA exploratory research group since 2020. AYANA is an interdisciplinary project using knowledge in stochastic modeling, image processing, artificial intelligence, remote sensing and embedded electronics/computing.
She was full professor (PR1) at SUPAERO (ISAE) in Toulouse from 1999 to 2020. She received a Doctor Honoris Causa degree from the University of Szeged in Hungary in November 2021, and 3 times the Excellence Award from University of Nice (now called Université Côte d´Azur) in 2016, 2019 and 2020. She is a Fellow of the IEEE (2003- ), the EURASIP (2019- ) and the IAPR (2020- ), and IEEE SP Society Distinguished Lecturer (2016-2017). She has been a member of the editorial boards of the Foundation and Trends in Signal Processing since 2007 and of the IEEE Signal Processing Magazine since September 2018. She has been member-at-large of the Awards Board of the IEEE SP Society since 2020. Finally, she has been a member of the Best Paper Award Committee for EURASIP JIVP in 2021.
She was part of the organizing committees of the workshop EarthVision (co-chair) at IEEE CVPR 2017 (Honolulu, USA) and GRETSI 2017 symposium (Juan les Pins, France). She is scientific advisor and co-organizer of ISPRS 2020 (virtual), 2021 (virtual) and 2022 congress (Nice, France) and technical co-chair of IEEE-EURASIP EUSIPCO 2021 (virtual, Dublin, Ireland).
Her main research interest is in image processing using probabilistic models. She also works on parameter estimation, statistical learning and optimization techniques, and artificial intelligence.
For more information see http://www-sop.inria.fr/members/Josiane.Zerubia/index-eng.html