Tatsumi Uezato
Tatsumi Uezato
RIKEN AIP
Adresse e-mail validée de uni.sydney.edu.au
Titre
Citée par
Citée par
Année
A novel spectral unmixing method incorporating spectral variability within endmember classes
T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan
IEEE Transactions on Geoscience and Remote Sensing 54 (5), 2812-2831, 2015
282015
Hyperspectral image unmixing with LiDAR data-aided spatial regularization
T Uezato, M Fauvel, N Dobigeon
IEEE Transactions on Geoscience and Remote Sensing 56 (7), 4098-4108, 2018
172018
A novel endmember bundle extraction and clustering approach for capturing spectral variability within endmember classes
T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan
IEEE Transactions on Geoscience and Remote Sensing 54 (11), 6712-6731, 2016
172016
Incorporating spatial information and endmember variability into unmixing analyses to improve abundance estimates
T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan
IEEE Transactions on Image Processing 25 (12), 5563-5575, 2016
162016
Mineralogical mapping of southern Namibia by application of continuum-removal MSAM method to the HyMap data
S Oshigami, Y Yamaguchi, T Uezato, A Momose, Y Arvelyna, Y Kawakami, ...
International journal of remote sensing 34 (15), 5282-5295, 2013
162013
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity
T Uezato, M Fauvel, N Dobigeon
IEEE Transactions on Geoscience and Remote Sensing 57 (6), 3980-3992, 2019
152019
Multiple endmember spectral unmixing within a multi-task framework
T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan, S Schneider
2014 IEEE Geoscience and Remote Sensing Symposium, 3454-3457, 2014
52014
Spectral curve-based endmember extraction method
T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan
2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in …, 2015
12015
Guided Deep Decoder: Unsupervised Image Pair Fusion
T Uezato, D Hong, N Yokoya, W He
arXiv preprint arXiv:2007.11766, 2020
2020
Illumination Invariant Hyperspectral Image Unmixing Based on a Digital Surface Model
T Uezato, N Yokoya, W He
IEEE Transactions on Image Processing 29, 3652-3664, 2020
2020
Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability
T Uezato, M Fauvel, N Dobigeon
Remote Sensing 12 (14), 2326, 2020
2020
LiDAR-Guided Reduction Of Spectral Variability in Hyperspectral Imagery
S Kahraman, R Bacher, T Uezato, J Chanussot, A Tangel
2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution …, 2019
2019
A multiple endmember mixing model to handle spectral variability in hyperspectral unmixing
T Uezato, M Fauvel, N Dobigeon
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in …, 2018
2018
LiDAR-driven spatial regularization for hyperspectral unmixing
T Uezat, M Fauvel, N Dobigeon
IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018
2018
Prédiction des services écosystémiques dans les paysages agricoles par télédétection hyperspectrale
M Fauvel, R Duflot, T Uezato, N Dobigeon, A Vialatte, D Sheeren, ...
2018
Ecosystem services assessment using hyperspectral images
M Fauvel, T Uezato, R Duflot, N Dobigeon, A Vialatte, D Sheeren
5. Colloque de la Société Française de Photogrammétrie et Télédétection …, 2017
2017
Spectral curve-based endmember extraction method
A Chlingaryan, A Melkumyan, R Murphy, T Uezato
Institute of Electrical and Electronics Engineers (IEEE), 2017
2017
Unmixing of hyperspectral data by incorporating spectral variability and spatial information
T Uezato
University of Sydney, 2016
2016
Incorporating Spatial Information and Endmember Variability into Unmixing Analyses to Improve Abundance Estimates
A Chlingaryan, A Melkumyan, R Murphy, T Uezato
(IEEE) Institute of Electrical and Electronics Engineers, 2016
2016
Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.
Articles 1–19