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Proceedings Paper

Improving the accuracy of water and bottom properties derived from remote sensing reflectance via artificial neural network
Author(s): Mingrui Zhang; ZhongPing Lee; Jinyan Guan
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Paper Abstract

Artificial neural network has been proven a useful technique for deriving water and bottom properties from remote sensing upwelling radiance. Conventionally, a neural network is trained to minimize the overall mean square error of desired products. The approach does not explicitly take into account the change of spectral shapes of upwelling radiance. In this study, we have created four groups of training sets, two groups with ratios of Rrs( λi) to Rrs(557), and the others without. Ratios of Rrs( λi) to Rrs(557) for λi of 409nm, 438nm, 488nm, 507nm, 616nm, 665nm, 683nm, 712nm, 750nm and 779nm have been used as additional inputs in the training of neural networks. Trained neural networks were then applied to an independent testing set which was created for optically different coastal waters. The inclusion of 10 spectral ratios in the training significantly improves the accuracy of derived water depth H, backscattering coefficient bb(438) and the absorption coefficient a(438). The accuracy of the derived coefficients is 86%, 94% and 92%. Our results clearly show the importance for including spectral ratios in the neural network training process. Remote sensing upwelling radiance over the identified 11 spectral channels provides adequate information for the retrieval of water optical property coefficients when an artificial neural network approach is used.

Paper Details

Date Published: 5 October 2007
PDF: 8 pages
Proc. SPIE 6680, Coastal Ocean Remote Sensing, 668008 (5 October 2007); doi: 10.1117/12.731760
Show Author Affiliations
Mingrui Zhang, Winona State Univ. (United States)
ZhongPing Lee, Naval Research Lab. (United States)
Jinyan Guan, Winona State Univ. (United States)


Published in SPIE Proceedings Vol. 6680:
Coastal Ocean Remote Sensing
Robert J. Frouin; ZhongPing Lee, Editor(s)

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