FPGA Implementation of L<sub>1/2</sub> Sparsity Constrained Nonnegative Matrix Factorization Algorithm for Remotely Sensed Hyperspectral Image Analysis

FPGA Implementation of L<sub>1/2</sub> Sparsity Constrained Nonnegative Matrix Factorization Algorithm for Remotely Sensed Hyperspectral Image Analysis

Remotely sensed hyperspectral images provide data of the earth's surface components. The data provided is collected through airborne devices such as satellites with the capability to collect large amounts of data to be sent to ground stations for processing. The main disadvantage of this scenar...

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Bibliographic Details
Main Authors: Mostafa Guda, Safa Gasser, Mohamed S. El-Mahallawy, Khaled Shehata
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Field-programmable gate arrays (FPGAs)
hyperspectral unmixing
L⅟₂ nonnegative matrix factorization algorithm (NMF)
Online Access:https://ieeexplore.ieee.org/document/8957084/

Internet

https://ieeexplore.ieee.org/document/8957084/

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