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Proceedings Paper

Augmented logarithmic Gaussian process regression methodology for chlorophyll prediction
Author(s): Subhadip Dey; Sawon Pratiher; C. K. Mukherjee; Saon Banerjee; Arnab Chakraborty
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Paper 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.

Paper Details

Date Published: 24 May 2018
PDF: 6 pages
Proc. SPIE 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V, 106791E (24 May 2018); doi: 10.1117/12.2306266
Show Author Affiliations
Subhadip Dey, Indian Institute of Technology Kharagpur (India)
Sawon Pratiher, Indian Institute of Technology Kharagpur (India)
C. K. Mukherjee, Indian Institute of Technology Kharagpur (India)
Saon Banerjee, Bidhan Chandra Krishi Viswavidyalaya (India)
Arnab Chakraborty, RCC Institute of Information Technology (India)


Published in SPIE Proceedings Vol. 10679:
Optics, Photonics, and Digital Technologies for Imaging Applications V
Peter Schelkens; Touradj Ebrahimi; Gabriel Cristóbal, Editor(s)

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