FPGA Implementation of L1/2 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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Veröffentlicht in:	IEEE access Jg. 8; S. 12069 - 12083
Hauptverfasser:	Guda, Mostafa, Gasser, Safa, El-Mahallawy, Mohamed S., Shehata, Khaled
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Data analysis Datasets Earth Earth surface Factorization Field programmable gate arrays Field-programmable gate arrays (FPGAs) Ground stations Ground truth Hyperspectral imaging hyperspectral unmixing Image analysis Indexes L⅟₂ nonnegative matrix factorization algorithm (NMF) Mathematical analysis Matrix methods Microprocessors Remote sensing Satellites Sparsity Spectra
ISSN:	2169-3536
Online-Zugang:	Volltext
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