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

• Published in: Remote Sensing
• Main Authors: Blix, Katalin, Eltoft, Torbjørn
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI 2018
• Subjects: VDP::Mathematics and natural science: 400::Physics: 430; VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430; ocean color; remote sensing; model selection; feature ranking; regression; Arctic
• Online Access: https://hdl.handle.net/10037/14038; https://doi.org/10.3390/rs10050775

First published in Remote Sensing . Source at https://doi.org/10.3390/rs10050775 . 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.