Publication Date:
2019-06-28
Description:
As the current fleet of meteorological satellites age, the accuracy of the imagery sensed on a spectral channel of the image scanning system is continually and progressively degraded by noise. In time, that data may even become unusable. We describe a novel approach to the reconstruction of the noisy satellite imagery according to empirical functional relationships that tie the spectral channels together. Abductive networks are applied to automatically learn the empirical functional relationships between the data sensed on the other spectral channels to calculate the data that should have been sensed on the corrupted channel. Using imagery unaffected by noise, it is demonstrated that abductive networks correctly predict the noise-free observed data.
Keywords:
METEOROLOGY AND CLIMATOLOGY
Type:
SAIC-94/1062
,
NASA. Goddard Space Flight Center, The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies; p 179-191
Format:
application/pdf
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