Tatsumi Uezato is a postdoctoral researcher at RIKEN Center for Advanced Intelligence Project (AIP), Japan. His research interests include hyperspectral image processing, remote sensing, and machine learning.
He is a member of the IEEE (2017), IEEE Geoscience and Remote Sensing Society (GRSS), and IEEE signal processing society (SPS). He is a reviewer of IEEE transactions on geoscience and remote sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Remote Sensing, Sensor and applied remote sensing.
|2019 Jan||-||Present||Postdoctoral researcher, RIKEN AIP|
|2017 Jan||-||2018 Dec||Postdoctoral researcher, IRIT|
|2016 Sep||-||2016 Dec||Research associate, The University of Sydney|
|2013 Feb||-||2016 Dec||Ph.D. in Engineering & IT, The University of Sydney|
T. Uezato, N. Yokoya, and W. He,
Illumination invariant hyperspectral image unmixing based on a digital surface model
IEEE Transactions on Image Processing (accepted for publication), 2019.
Abstract: Although many spectral unmixing models have been developed to address spectral variability caused by variable incident illuminations, the mechanism of the spectral variability is still unclear. This paper proposes an unmixing model, named illumination invariant spectral unmixing (IISU). IISU makes the first attempt to use the radiance hyperspectral data and a LiDAR-derived digital surface model (DSM) in order to physically explain variable illuminations and shadows in the unmixing framework. Incident angles, sky factors, visibility from the sun derived from the LiDAR-derived DSM support the explicit explanation of endmember variability in the unmixing process from radiance perspective. The proposed model was efficiently solved by a straightforward optimization procedure. The unmixing results showed that the other state-of-the-art unmixing models did not work well especially in the shaded pixels. On the other hand, the proposed model estimated more accurate abundances and shadow compensated reflectance than the existing models.