Quantitative Reconstruction and Denoising Method HyBER for Hyperspectral Image Data and Its Application to CRISM

Quantitative Reconstruction and Denoising Method HyBER for Hyperspectral Image Data and Its Application to CRISM

We propose a new algorithm, hypothesis-based estimation with regularization (HyBER), to reconstruct and denoise hyperspectral image data without extra statistical assumptions. The hypothesis test selects the best statistical model approximating measurements based on the data only. A regularized maxi...

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Bibliographic Details
Main Authors: Linyun He, Joseph A. O'Sullivan, Daniel V. Politte, Kathryn E. Powell, Raymond E. Arvidson
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Gaussian distributions
image reconstruction
maximum likelihood estimation
noise
Poisson distributions
remote sensing
Online Access:https://ieeexplore.ieee.org/document/8672185/

Internet

https://ieeexplore.ieee.org/document/8672185/

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