Article
Facing the Void: Overcoming Missing Data in Multi-View Imagery
Machado G, Pereira MB, Nogueira K & Dos Santos JA (2022) Facing the Void: Overcoming Missing Data in Multi-View Imagery. IEEE Access. https://doi.org/10.1109/access.2022.3231617
Contact details
- Email jefersson.dos.santos@stir.ac.uk
- Telephonec/o 7421
About me
About me
Jefersson A. dos Santos is currently an Assistant Professor (Lecturer) in Computing Science at the University of Stirling, Scotland, United Kingdom. He used to hold a position as Associate Professor at the Universidade Federal de Minas Gerais, Brazil (2013 - 2022). Jefersson got a Ph.D in Computer Science at the University of Campinas (Unicamp), Brazil, and at the University of Cergy-Pontoise, France, in 2013. He earned a Master's Degree in Computer Science from Unicamp in 2009 and a Bachelor Degree in Computer Science from University of Mato Grosso do Sul (UEMS), Brazil, in 2006. Jefersson is an IEEE Senior Member. He is currently an Associate Editor for the IEEE Geoscience and Remote Sensing Letters and Co-Chair for the ISPRS Working Group for AI/ML for Geospatial Data. Jefersson also serves as a PC member for several refereed journals and conferences and is currently the vice president of the IEEE GRSS Brazilian Chapter. Jefersson used to hold the prestigious CNPq Productivity Research Scholarship (2016 - 2022). In 2021, he was awarded a competitive research grant from the Serrapilheira Institute, Brazil. His research interests include remote sensing image processing, computer vision and machine learning. Jefersson is the founder of the Laboratory of Pattern Recognition and Earth Observation (PATREO) at the Department of Computer Science, UFMG, Brazil.
Other Academic Activities
Associate Editor for IEEE Geoscience and Remote Sensing Letters
IEEE (Institute of Electrical and Electronics Engineers)
https://ieeexplore.ieee.org/…sp?punumber=8859
IEEE Geoscience and Remote Sensing Letters (GRSL) publishes short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sending the earth, oceans, atmosphere and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of “extended objects” or “multimedia” such as animations to enhance the shorter papers.
Other Project
Serrapilheira Institute Grantee - 4th call
https://serrapilheira.org/…with-up-to-r700/
The Serrapilheira Institute is the first private non-profit institution geared towards fostering science in Brazil. It was founded to place more value on scientific knowledge and to increase science visibility. The idea is to stimulate a culture of science by acting in two areas: science itself and science outreach.
Professional membership
IEEE Senior Member
IEEE (Institute of Electrical and Electronics Engineers)
Senior Member is IEEE's distinguished membership grade approved and assigned by the IEEE Admission and Advancement Committee only for IEEE members with 10 years in their profession and at least 5 years of significant technical or professional performance.
Research
My current research interests include machine learning, computer vision, image processing, and remote sensing.
My research balances both computational challenges and their applications. Besides always aiming at excellence and impacting outcomes in Computer Science, I am especially interested in inter-, cross-, multidisciplinary research. I see the connection with other areas of knowledge as an opportunity to develop cutting-edge research with high possibilities for impacting directly on economic, social, and environmental aspects.
Outputs (5)
Outputs
Article
Self-Supervised Learning for Seismic Image Segmentation From Few-Labeled Samples
Monteiro BAA, Oliveira H & dos Santos J (2022) Self-Supervised Learning for Seismic Image Segmentation From Few-Labeled Samples. IEEE Geoscience and Remote Sensing Letters, 19, pp. 1-5. https://doi.org/10.1109/lgrs.2022.3193567
Article
Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks
Nogueira K, Dalla Mura M, Chanussot J, Schwartz WR & dos Santos JA (2019) Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks. IEEE Transactions on Geoscience and Remote Sensing, 57 (10), pp. 7503-7520. https://doi.org/10.1109/tgrs.2019.2913861
Article
Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks
Nogueira K, dos Santos JA, Menini N, Silva TSF, Morellato LPC & da S Torres R (2019) Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks. IEEE Geoscience and Remote Sensing Letters, 16 (10), pp. 1665-1669. https://doi.org/10.1109/lgrs.2019.2903194
Article
Exploiting ConvNet Diversity for Flooding Identification
Nogueira K, Fadel SG, Dourado IC, Werneck RdO, Munoz JAV, Penatti OAB, Calumby RT, Li LT, dos Santos JA & Torres RdS (2018) Exploiting ConvNet Diversity for Flooding Identification. IEEE Geoscience and Remote Sensing Letters, 15 (9), pp. 1446-1450. https://doi.org/10.1109/lgrs.2018.2845549