Next Article in Journal
SAR Target Recognition in Large Scene Images via Region-Based Convolutional Neural Networks
Next Article in Special Issue
Remotely Sensing the Biophysical Drivers of Sardinella aurita Variability in Ivorian Waters
Previous Article in Journal
Erratum: Nasonova, S. et al. Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice. Remote Sens. 2018, 10, 37
Previous Article in Special Issue
A Statistical Modeling Framework for Characterising Uncertainty in Large Datasets: Application to Ocean Colour
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(5), 775; https://doi.org/10.3390/rs10050775

Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval

UiT the Arctic University of Norway, P.O. box 6050 Langnes, NO-9037 Tromsø, Norway
*
Author to whom correspondence should be addressed.
Received: 13 March 2018 / Revised: 4 April 2018 / Accepted: 16 May 2018 / Published: 17 May 2018
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
Full-Text   |   PDF [8863 KB, uploaded 17 May 2018]   |  

Abstract

Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland waters. This is done by using various regression algorithms to retrieve water quality parameters from remotely sensed multi-spectral data for the given sensor and environment. There is a great number of such algorithms for estimating water quality parameters with different performances. Hence, choosing the most suitable model for a given purpose can be challenging. This is especially the fact for optically complex aquatic environments. In this paper, we present a concept to an Automatic Model Selection Algorithm (AMSA) aiming at determining the best model for a given matchup dataset. AMSA automatically chooses between regression models to estimate the parameter in interest. AMSA also determines the number and combination of features to use in order to obtain the best model. We show how AMSA can be built for a certain application. The example AMSA we present here is designed to estimate oceanic Chlorophyll-a for global and optically complex waters by using four Machine Learning (ML) feature ranking methods and three ML regression models. We use a synthetic and two real matchup datasets to find the best models. Finally, we use two images from optically complex waters to illustrate the predictive power of the best models. Our results indicate that AMSA has a great potential to be used for operational purposes. It can be a useful objective tool for finding the most suitable model for a given sensor, water quality parameter and environment. View Full-Text
Keywords: ocean color; remote sensing; model selection; feature ranking; regression ocean color; remote sensing; model selection; feature ranking; regression
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Blix, K.; Eltoft, T. Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval. Remote Sens. 2018, 10, 775.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top