Learning Relevant Features of Optical Water Types.

This work introduces a novel method that makes use of machine learning (ML) techniques to classify hyper- and multi spectral observations into optical water types (OWTs). Classification was done using $k$ -means clustering, which was followed by a feature relevance step based on the sensitivity analysis (SA) of the predictive mean and variance function of a Gaussian process (GP) regression model. The method was used both in training and predictive mode. The latter allows applying the approach for new unlabeled observations, so that the OWTs and the associated relevant features can automatically be assessed. The methods were studied on hyperspectral synthesized and in situ Arctic data, and were further evaluated on a test image acquired over Arctic seas. Good empirical results encourage wide adoption of the methodology to be applied in operational processing and assessment of water types. [ABSTRACT FROM AUTHOR]