0000000000216955

AUTHOR

Gustau Camps Valls

showing 2 related works from this author

Interpolation and Gap Filling of Landsat Reflectance Time Series

2018

Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions. Image fusion in general and downscaling/blending in particular allow to combine different multiresolution datasets. We present here an optimal interpolation approach to generate smoothed and gap-free time series of Landsat reflectance data. We fuse MODIS (moderate-resolution imaging spectroradiometer) and Landsat data globally using the Google Earth Engine (GEE) platform. The optimal interpolator exploits GEE ability to ingest large amounts of data (Landsat climatologies) and uses simple linear operations that scale easily in the cloud. The approach shows very good result…

Signal Processing (eess.SP)Image fusion010504 meteorology & atmospheric sciencesComputer scienceMultispectral image0211 other engineering and technologies02 engineering and technology01 natural sciencesReflectivitySpectroradiometerFOS: Electrical engineering electronic engineering information engineeringTime seriesElectrical Engineering and Systems Science - Signal ProcessingScale (map)Image resolution021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingInterpolationDownscaling
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Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval

2018

The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. Statistical models performance is compromised because of the extremely high spectral dimensionality and the high number of variables to be predicted simultaneously across the atmospheric column. All this poses a challenge for selecting and studying optimal models and processing schemes. Earlier work has shown non-linear models such as kernel methods and neural networks perform w…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceFeature extraction0211 other engineering and technologiesTranfer learningFOS: Physical sciences02 engineering and technologyAtmospheric modelInfrared atmospheric sounding interferometercomputer.software_genreConvolutional neural networkMachine Learning (cs.LG)0202 electrical engineering electronic engineering information engineeringInfrared measurements021101 geological & geomatics engineeringArtificial neural networkStatistical modelNumerical weather predictionParameter retrievalPhysics - Atmospheric and Oceanic PhysicsKernel method13. Climate actionAtmospheric and Oceanic Physics (physics.ao-ph)Convolutional neural networks020201 artificial intelligence & image processingData miningcomputerCurse of dimensionalityIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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