Show simple item record

dc.contributor.authorHeestermans Svendsen, Daniel
dc.contributor.authorMorales Álvarez, Pablo 
dc.contributor.authorMolina Soriano, Rafael
dc.identifier.citationSvendsen, 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. []es_ES
dc.description.abstractParameter 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.es_ES
dc.description.sponsorshipEuropean Research Council (ERC) 647423es_ES
dc.description.sponsorshipSpanish Ministry of Economy and Competitiveness TIN2015-64210-R DPI2016-77869-C2-2-Res_ES
dc.description.sponsorshipSpanish Excellence Network TEC2016-81900-REDTes_ES
dc.description.sponsorshipLa Caixa Banking Foundation (Barcelona, Spain) 100010434 LCF-BQ-ES17-11600011es_ES
dc.rightsAtribución 3.0 España*
dc.subjectModel inversiones_ES
dc.subjectStatistical retrievales_ES
dc.subjectDeep Gaussian Processeses_ES
dc.subjectMachine learninges_ES
dc.subjectTemperature es_ES
dc.subjectChlorophyll contentes_ES
dc.subjectInorganic suspended matteres_ES
dc.subjectColoured dissolved matteres_ES
dc.subjectInfrared sounderes_ES
dc.subjectCopernicus programmees_ES
dc.titleDeep Gaussian processes for biogeophysical parameter retrieval and model inversiones_ES

Files in this item


This item appears in the following Collection(s)

Show simple item record

Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España