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Proceedings Paper

Hyperspectral compression-decompression using artificial neural networks
Author(s): David G. Wagner; Siza D. Tumbo
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Paper Abstract

The storage and retrieval of data from the hard disk and transfer speed of data from a microcomputer-based sensor to a personal computer are of critical importance for on-the-go sensing. A neural network-based spectral bands decompression model has been developed to optimize acquisition and retrieval of hyperspectral data. The model decompresses or predicts 201 spectral bands from 25 spectral bands between 407 and 940 nm. The model showed strong correlation between decompressed and actual hyperspectral patterns (coefficient of correlation (r2) equals 0.99 and root mean square error (RMSE) equals 0.0004). The decompressed or predicted hyperspectral reflectance patterns were fed into a neural network-based model that predicts chlorophyll readings. The decompressed hyperspectral reflectance patterns showed good correlation to chlorophyll readings (r2 equals 0.89, RMSE equals 1.32 SPAD units). The spectral bands decompression model reduces the number of hyperspectral bands to be downloaded from the spectrometer, stored, or retrieved from the hard disk by 87%.

Paper Details

Date Published: 29 December 2000
PDF: 12 pages
Proc. SPIE 4203, Biological Quality and Precision Agriculture II, (29 December 2000); doi: 10.1117/12.411736
Show Author Affiliations
David G. Wagner, The Pennsylvania State Univ. (United States)
Siza D. Tumbo, The Pennsylvania State Univ. (United States)

Published in SPIE Proceedings Vol. 4203:
Biological Quality and Precision Agriculture II
James A. DeShazer; George E. Meyer, Editor(s)

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