55 citations · 136 across the 5 of their papers we have counts for
5 papers
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution
Xiaoyu Dong, Naoto Yokoya, Longguang Wang +1
Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high…
Illumination invariant hyperspectral image unmixing based on a digital surface model
Tatsumi Uezato, Naoto Yokoya, Wei He
Although many spectral unmixing models have been developed to address spectral variability caused by variable incident illuminations, the mechanism of the spectral variability is s…
Guided Deep Decoder: Unsupervised Image Pair Fusion
Tatsumi Uezato, Danfeng Hong, Naoto Yokoya +1
The fusion of input and guidance images that have a tradeoff in their information (e.g., hyperspectral and RGB image fusion or pansharpening) can be interpreted as one general prob…
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity
Tatsumi Uezato, Mathieu Fauvel, Nicolas Dobigeon
Spectral variability is one of the major issue when conducting hyperspectral unmixing. Within a given image composed of some elementary materials (herein referred to as endmember c…
Hyperspectral image unmixing with LiDAR data-aided spatial regularization
Tatsumi Uezato, Mathieu Fauvel, Nicolas Dobigeon
Spectral unmixing methods incorporating spatial regularizations have demonstrated increasing interest. Although spatial regularizers which promote smoothness of the abundance maps…