Improving the accuracy of water and bottom properties derived from remote sensing reflectance via artificial neural network

Mingrui Zhang; ZhongPing Lee; Jinyan Guan

doi:10.1117/12.731760

5 October 2007 Improving the accuracy of water and bottom properties derived from remote sensing reflectance via artificial neural network

Mingrui Zhang, ZhongPing Lee, Jinyan Guan

Author Affiliations +

Proceedings Volume 6680, Coastal Ocean Remote Sensing; 668008 (2007) https://doi.org/10.1117/12.731760
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

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( λ_i) to Rrs(557), and the others without. Ratios of Rrs( λ_i) to Rrs(557) for λ_i 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 b_b(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.

Citation Download Citation

Mingrui Zhang, ZhongPing Lee, and Jinyan Guan "Improving the accuracy of water and bottom properties derived from remote sensing reflectance via artificial neural network", Proc. SPIE 6680, Coastal Ocean Remote Sensing, 668008 (5 October 2007); https://doi.org/10.1117/12.731760

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available