Augmented logarithmic Gaussian process regression methodology for chlorophyll prediction

Subhadip  Dey; Sawon Pratiher; C. K. Mukherjee; Saon  Banerjee; Arnab  Chakraborty

doi:10.1117/12.2306266

24 May 2018 Augmented logarithmic Gaussian process regression methodology for chlorophyll prediction

Subhadip Dey, Sawon Pratiher, C. K. Mukherjee, Saon Banerjee, Arnab Chakraborty

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Proceedings Volume 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V; 106791E (2018) https://doi.org/10.1117/12.2306266
Event: SPIE Photonics Europe, 2018, Strasbourg, France

Abstract

In aquaculture engineering, estimation of chlorophyll concentration is of utmost importance for water quality monitoring. For a particular area, its concentration is a direct manifestation of the region suitability for fish farming. In literature different parametric and non parametric methods have been studied for chlorophyll concentration prediction. In this paper we have pre-processed the remote sensing data by logarithmic transformation which enhances the data correlation and followed by Gaussian Process Regression (GPR) based forecasting. The proposed methodology is validated on Sea-viewing Wide Field-of-View Sensor (SeaWIFS) and the NASA operational Moderate Resolution Imaging Spectro-radiometer onboard AQUA (MODIS-Aqua) data-sets. Experimental result shows the proposed method's efficacy in enhanced accuracy using the projected data.

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Subhadip Dey, Subhadip Dey, Sawon Pratiher, Sawon Pratiher, C. K. Mukherjee, C. K. Mukherjee, Saon Banerjee, Saon Banerjee, Arnab Chakraborty, Arnab Chakraborty, } "Augmented logarithmic Gaussian process regression methodology for chlorophyll prediction", Proc. SPIE 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V, 106791E (24 May 2018); doi: 10.1117/12.2306266; https://doi.org/10.1117/12.2306266

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