N
Naoto Yokoya
Researcher at University of Tokyo
Publications - 179
Citations - 9851
Naoto Yokoya is an academic researcher from University of Tokyo. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 35, co-authored 155 publications receiving 5102 citations. Previous affiliations of Naoto Yokoya include German Aerospace Center & Tokyo University of Agriculture and Technology.
Papers
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Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion
TL;DR: Simulations with various image data sets demonstrate that the CNMF algorithm can produce high-quality fused data both in terms of spatial and spectral domains, which contributes to the accurate identification and classification of materials observed at a high spatial resolution.
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Hyperspectral Pansharpening: A Review
Laetitia Loncan,Luís B. Almeida,Jose M. Bioucas-Dias,Xavier Briottet,Jocelyn Chanussot,Nicolas Dobigeon,Sophie Fabre,Wenzhi Liao,Giorgio Licciardi,Miguel Simoes,Jean-Yves Tourneret,Miguel Angel Veganzones,Gemine Vivone,Qi Wei,Naoto Yokoya +14 more
TL;DR: In this article, the state-of-the-art multispectral pansharpening techniques for hyperspectral data were compared with some of the state of the art methods for multi-spectral panchambering.
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More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
TL;DR: A baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework that is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs).
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Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art
Pedram Ghamisi,Naoto Yokoya,Jun Li,Wenzhi Liao,Sicong Liu,Javier Plaza,Behnood Rasti,Antonio Plaza +7 more
TL;DR: Rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide.
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Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature
TL;DR: Ten state-of-the-art HS-MS fusion methods are compared by assessing their fusion performance both quantitatively and visually and the generalizability and versatility of the fusion algorithms are evaluated.