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19 March 2009 Neural network approach for mobile bay water quality mapping with spaceborne measurements
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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.
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He Yang and Qian Du "Neural network approach for mobile bay water quality mapping with spaceborne measurements", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430I (19 March 2009); https://doi.org/10.1117/12.818377
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