<title>Algorithm for the estimation of oceanic chlorophyll concentration from hyperspectral data through purpose-oriented feature extraction</title>

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Remotely sensed data are used for global estimation of oceanic chlorophyll concentration, from which biomass productivity in ocean is estimated. A conventional (Gordon's) method uses the ratio of green to blue bands as an indicator of the chlorophyll concentration. This method is not accurate, especially when phytoplankton is not dominant. We devise a method through our purpose-oriented feature extraction method which is much more accurate than the conventional method even when the other components than chlorophyll are not negligible. The basic idea of our method is to fuse each dimension of hyper-spectral data to produce a value which describes the chlorophyll concentration almost independent of other components. We confirmed by simulation that our algorithm gives two to ten times more accurate results than the conventional method does. It is another prominent feature that our method is wholly systematic, and widely applicable.

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