24 May 2018 Augmented logarithmic Gaussian process regression methodology for chlorophyll prediction
Author Affiliations +
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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
6 PAGES


SHARE
Back to Top