Intelligent Data Thinning Algorithms for Satellite Imagery

This paper presents a study on intelligent data thinning for satellite data. In particular, the focus is on the thinning of the Atmospheric Infrared Sounder (AIRS) profiles. A direct thinning method is first applied to a synthetic data set in order to identify optimal data selection strategies. Expe...

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Bibliographic Details
Main Authors: Zavodsky, B., Lazarus, S., Xiang Li, Lueken, M., Splitt, M., Ramachandran, R., Movva, S., Graves, S., Lapenta, W.
Format: Conference Proceeding
Language:English
Subjects:
Acoustic testing
Error analysis
Geoscience
Information retrieval
Information technology
NASA
Satellites
Space technology
Spatial resolution
Thermodynamics
Online Access:Request full text
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Summary:This paper presents a study on intelligent data thinning for satellite data. In particular, the focus is on the thinning of the Atmospheric Infrared Sounder (AIRS) profiles. A direct thinning method is first applied to a synthetic data set in order to identify optimal data selection strategies. Experiments on synthetic data suggest that a thinned data set should combine homogeneous samples, and high gradient and variance of gradient samples for optimal performance. This result leads to the modification of our previously developed Density Adjustment Data Thinning algorithm (DADT). The modified DADT (mDADT) algorithm is used to thin the AIRS profiles. Experiments are conducted to compare the thinning performances of mDADT with two simple thinning algorithms. Experiment results show that mDADT algorithm performs better than the two simple thinning algorithms, especially over the regions of significant atmospheric features.
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2008.4779430