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Proceedings Paper

Neural network approach for mobile bay water quality mapping with spaceborne measurements
Author(s): He Yang; Qian Du
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Paper Abstract

Remote sensing techniques are well suited to quantify the spatial variability of coastal water quality. The correlation between remotely sensed data in the visible to near-infrared (VNIR) bands and in situ water measurements are well studied. Due to the high spatial variation and fine waterbody structure along shorelines, it may be beneficial to use remotely sensed images with higher spatial resolution, such as the Landsat data with 30m resolution. In this research, we investigate the traditional approaches, such as regression analysis, in the mapping of water quality (e.g., total suspended sediments (TSS), turbidity, and chlorophyll A). In particular, we also develop an approach based on neural network to generate additional bands, which can further improve the mapping accuracy.

Paper Details

Date Published: 19 March 2009
PDF: 11 pages
Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430I (19 March 2009); doi: 10.1117/12.818377
Show Author Affiliations
He Yang, Mississippi State Univ. (United States)
Qian Du, Mississippi State Univ. (United States)


Published in SPIE Proceedings Vol. 7343:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
Harold H. Szu; F. Jack Agee, Editor(s)

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