Rate My Professor Naoto Yokoya

The University of Tokyo

5.0

2 reviews · out of 5

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5.08/20/2025

Brings real-world insights to the classroom.

5.04/30/2025

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About Naoto

Naoto Yokoya is a Professor in the Department of Complexity Science and Engineering at the Graduate School of Frontier Sciences, The University of Tokyo, as well as in the Department of Information Science at the School of Science. He concurrently serves as Team Director of the Geoinformatics Team at the RIKEN Center for Advanced Intelligence Project. Yokoya earned his B.Eng. in 2008, M.Eng. in 2010, and D.Eng. in 2013, all from the Department of Aeronautics and Astronautics at The University of Tokyo.

His academic career at The University of Tokyo began as a JSPS Research Fellow from 2012 to 2013 and Assistant Professor from 2013 to 2017, followed by Lecturer from 2020 to 2022 and Associate Professor from 2022 to 2025, culminating in his current professorship since April 2025. Internationally, he was an Alexander von Humboldt Research Fellow at the German Aerospace Center and Technical University of Munich from 2015 to 2017. At RIKEN, he advanced from Unit Leader in 2018 to Team Director in 2023. Additional roles include Visiting Scholar at the National Food Research Institute in 2013-2014 and Visiting Associate Professor at Tokyo University of Agriculture and Technology from 2019 to 2020.

Yokoya's research centers on visual intelligence, integrating computer vision, machine learning, and data fusion to understand physical, geometric, and semantic structures from visual and multimodal data, with a focus on remote sensing for Earth observation, including hyperspectral image processing, scene understanding, and applications in disaster assessment and environmental monitoring. His influential publications include "Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion" (2011, 1238 citations), "Hyperspectral pansharpening: A review" (2015, 949 citations), "An augmented linear mixing model to address spectral variability for hyperspectral unmixing" (2018, 1005 citations), "Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art" (2019, 690 citations), and "SpectralGPT: Spectral remote sensing foundation model" (2024, 974 citations). With over 20,000 citations on Google Scholar, he has been named a Clarivate Highly Cited Researcher in Geosciences from 2022 to 2025.

His honors include the Young Scientists’ Award from the Minister of Education, Culture, Sports, Science and Technology (2024), Funai Academic Award (2024), 1st place in the 2017 IEEE GRSS Data Fusion Contest, Alexander von Humboldt Research Fellowship (2015), and Best Presentation Awards from the Remote Sensing Society of Japan (2011, 2012, 2019).