Improved technique for statistically optimum inversion of optical data in presence of measurements and model noise

Oleg Dubovik; Tatjana Lapyonok; Sergey Oshchepkov

doi:10.1117/12.196675

23 December 1994 Improved technique for statistically optimum inversion of optical data in presence of measurements and model noise

Oleg Dubovik, Tatjana Lapyonok, Sergey Oshchepkov

Author Affiliations +

Proceedings Volume 2309, Passive Infrared Remote Sensing of Clouds and the Atmosphere II; (1994) https://doi.org/10.1117/12.196675
Event: Satellite Remote Sensing, 1994, Rome, Italy

Abstract

The problems of optimum inversion in the presence of random noise are analyzed. Two main kinds of noise are considered: the random errors of measurements and random errors of physical model. It is studied the optimization of the numerical inverse problem solution concerning both noises. Using the statistical estimation ideas is discussed for this consideration. Specific features of every noise to influence on the limitation of information content of the optic experiment and on implementation of inversion are distinguished. The quantitative criteria to evaluate information content of input data and procedure of their interpretation are proposed. The latter is aimed to optimize the solution in presence of random errors of the model as well as errors of measurements and, moreover, to correct used model by the measurements being interpreted. An arbitrary accompanied and a priori information can be used. For example, a priori estimations of the sought and model parameters, correlations between them, non-negativity of values etc., can be included. The peculiarity of the inversion method is an essentially large number of variables and increased stability should be provided. The original iterative process of linear inversion characteristic to statistical optimizations are being proposed for this in the algorithm elaborating.

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Oleg Dubovik, Tatjana Lapyonok, and Sergey Oshchepkov "Improved technique for statistically optimum inversion of optical data in presence of measurements and model noise", Proc. SPIE 2309, Passive Infrared Remote Sensing of Clouds and the Atmosphere II, (23 December 1994); https://doi.org/10.1117/12.196675

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