6533b7cefe1ef96bd1257018
RESEARCH PRODUCT
Spatial noise-aware temperature retrieval from infrared sounder data
Gustau Camps-vallsAllan Aasbjerg NielsenDavid Malmgren-hansenValero Laparrasubject
Signal Processing (eess.SP)FOS: Computer and information sciencesComputer Science - Machine Learningbusiness.industryComputer scienceDimensionality reductionFeature extraction0211 other engineering and technologiesWord error ratePattern recognitionRegression analysis02 engineering and technologyMachine Learning (cs.LG)Principal component analysisLinear regression0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligenceElectrical Engineering and Systems Science - Signal ProcessingbusinessSpatial analysis021101 geological & geomatics engineeringdescription
In this paper we present a combined strategy for the retrieval of atmospheric profiles from infrared sounders. The approach considers the spatial information and a noise-dependent dimensionality reduction approach. The extracted features are fed into a canonical linear regression. We compare Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) for dimensionality reduction, and study the compactness and information content of the extracted features. Assessment of the results is done on a big dataset covering many spatial and temporal situations. PCA is widely used for these purposes but our analysis shows that one can gain significant improvements of the error rates when using MNF instead. In our analysis we also investigate the relationship between error rate improvements when including more spectral and spatial components in the regression model, aiming to uncover the trade-off between model complexity and error rates.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-12-09 |