Hybrid hyperspectral image compression technique for non-iterative factorized tensor decomposition and principal component analysis: application for NASA's AVIRIS data

DOI10.1007/S10596-019-09855-2zbMath1425.94015OpenAlexW2963278311WikidataQ127447303 ScholiaQ127447303MaRDI QIDQ2009825

Yanyan Li

Publication date: 2 December 2019

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-019-09855-2

zbMATH Keywords

waveletPCA3DPSNRHSIRMSE

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (1)

Hybrid hyperspectral image compression technique for non-iterative factorized tensor decomposition and principal component analysis: application for NASA's AVIRIS data

Uses Software

• Simulink
• Unnamed Item

Cites Work

• Unnamed Item
• Principal component analysis.
• Hybrid hyperspectral image compression technique for non-iterative factorized tensor decomposition and principal component analysis: application for NASA's AVIRIS data
• Hyperspectral Image Compression: Adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

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