Hyperspectral Shadow Removal via Nonlinear Unmixing

Hyperspectral Shadow Removal via Nonlinear Unmixing
Title:
Hyperspectral Shadow Removal via Nonlinear Unmixing
Other Titles:
IEEE Geoscience and Remote Sensing Letters
Publication Date:
23 April 2020
Citation:
M. Zhao, J. Chen and S. Rahardja, "Hyperspectral Shadow Removal via Nonlinear Unmixing," in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2020.2987353.
Abstract:
Removing shadows that are often present in remotely sensed hyperspectral images is important for both enhancing the interpretability of the data and further target analysis. Shadow removal approaches based on spectral unmixing have been proposed in the literature using the linear mixture model. However, objects that produce shadows may also introduce light scattering, and the higher order interactions of photons can cause nonlinearity. This letter integrates the nonlinear hyperperspectral unmixing into the unmixing-based shadow removal, and the effects of applying typical nonlinear algorithms within the approach are investigated. The usefulness of nonlinear unmixing in hyperspectral shadow removal is verified based on the results of applications to both laboratory-created real data and actual airborne data.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1558-0571
1545-598X
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