Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/20.500.11765/14862
Statistical Atmospheric Parameter Retrieval Largely Benefits from Spatial-Spectral Image Compression
Título : Statistical Atmospheric Parameter Retrieval Largely Benefits from Spatial-Spectral Image Compression
Autor : García Sobrino, JoaquínSerra-Sagristà, JoanLaparra, ValeroCalbet, Xavier ORCID RESEARCHERID SCOPUSID Autor AEMETCamps-Valls, Gustau
Palabras clave : Infrared Atmospheric Sounding Interferometer; Statistical retrieval; Kernel Methods; Near-Lossless Compression; Lossy Compression; JPEG 2000; Spectral Transforms
Fecha de publicación : 2017
Editor: Institute of Electrical and Electronics Engineers
Citación : IEEE Transactions on Geoscience and Remote Sensing. 2017, 55(4), p. 2213-2224
Versión del editor: https://doi.org/10.1109/TGRS.2016.2639099
Resumen : The Infrared Atmospheric Sounding Interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System (EPS). Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, IASI collects rich spectral information to derive temperature and moisture profiles –among other relevant trace gases–, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial-spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bitrates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors.
URI : http://hdl.handle.net/20.500.11765/14862
ISSN : 0196-2892
1558-0644
Colecciones: Artículos científicos 2015-2018


Ficheros en este ítem:
  Fichero Descripción Tamaño Formato  
IEETRAGEO_Calbet_2017...
1,32 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo del ítem



Los ítems de Arcimis están protegidos por una Licencia Creative Commons, salvo que se indique lo contrario.

Repositorio Arcimis
Nota Legal Contacto y sugerencias