Multimodal Data Fusion Using Non-Sparse Multi-Kernel Learning With Regularized Label Softening

Multimodal Data Fusion Using Non-Sparse Multi-Kernel Learning With Regularized Label Softening

Due to the need of practical application, multiple sensors are often used for data acquisition, so as to realize the multimodal description of the same object. How to effectively fuse multimodal data has become a challenge problem in different scenarios including remote sensing. Nonsparse multi-Kern...

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Bibliographic Details
Main Authors: Peihua Wang, Chengyu Qiu, Jiali Wang, Yulong Wang, Jiaxi Tang, Bin Huang, Jian Su, Yuanpeng Zhang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Label softening
manifold learning
multi-Kernel learning
remote sensing
semantic-based multimodal fusion
Online Access:https://ieeexplore.ieee.org/document/9449986/

Internet

https://ieeexplore.ieee.org/document/9449986/

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