Deep Gaussian processes for biogeophysical parameter retrieval and model inversion
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Materia
Model inversion Statistical retrieval Deep Gaussian Processes Machine learning Moisture Temperature Chlorophyll content Inorganic suspended matter Coloured dissolved matter Infrared sounder IASI Sentinels Copernicus programme
Date
2020-06-09Referencia bibliográfica
Svendsen, D. H., Morales-Álvarez, P., Ruescas, A. B., Molina, R., & Camps-Valls, G. (2020). Deep Gaussian processes for biogeophysical parameter retrieval and model inversion. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 68-81. [https://doi.org/10.1016/j.isprsjprs.2020.04.014]
Sponsorship
European Research Council (ERC) 647423; Spanish Ministry of Economy and Competitiveness TIN2015-64210-R DPI2016-77869-C2-2-R; Spanish Excellence Network TEC2016-81900-REDT; La Caixa Banking Foundation (Barcelona, Spain) 100010434 LCF-BQ-ES17-11600011Abstract
Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently,
different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical
inversion with in situ data that often results in problems with extrapolation outside the study area; and the most
widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine
learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different
existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have
provided useful and informative solutions to such RTM inversion problems. This is in large part due to the
confidence intervals they provide, and their predictive accuracy. However, RTMs are very complex, highly
nonlinear, and typically hierarchical models, so that very often a single (shallow) GP model cannot capture
complex feature relations for inversion. This motivates the use of deeper hierarchical architectures, while still
preserving the desirable properties of GPs. This paper introduces the use of deep Gaussian Processes (DGPs) for
bio-geo-physical model inversion. Unlike shallow GP models, DGPs account for complicated (modular, hierarchical) processes, provide an efficient solution that scales well to big datasets, and improve prediction accuracy over their single layer counterpart. In the experimental section, we provide empirical evidence of performance for the estimation of surface temperature and dew point temperature from infrared sounding data, as well
as for the prediction of chlorophyll content, inorganic suspended matter, and coloured dissolved matter from
multispectral data acquired by the Sentinel-3 OLCI sensor. The presented methodology allows for more expressive forms of GPs in big remote sensing model inversion problems.