Paper Info
Title
Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval.
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.
Year
DOI
Venue
2018
10.3390/rs10050775
REMOTE SENSING
Keywords
Field
DocType
ocean color,remote sensing,model selection,feature ranking,regression
Ocean color,Satellite,Content retrieval,Regression,Regression analysis,Feature ranking,Model selection,Algorithm,Artificial intelligence,Geology,Optical imaging,Machine learning
Journal
Volume
Issue
ISSN
10
5
2072-4292
Citations 
PageRank 
References 
2
0.37
13
Authors
2
Name
Order
Citations
PageRank
Katalin Blix1102.67
Torbjørn Eltoft258348.56