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

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.

Citation Download Citation

Subhadip Dey, Sawon Pratiher, C. K. Mukherjee, Saon Banerjee, and 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); https://doi.org/10.1117/12.2306266

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