Title
Hyperspectral Shadow Removal via Nonlinear Unmixing
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 interact...
Year
DOI
Venue
2021
10.1109/LGRS.2020.2987353
IEEE Geoscience and Remote Sensing Letters
Keywords
DocType
Volume
Hyperspectral imaging,Imaging,Analytical models,Photonics,Image color analysis,Lighting
Journal
18
Issue
ISSN
Citations 
5
1545-598X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Min Zhao1215.73
Jie Chen224.10
Susanto Rahardja3652102.05