Remote sensing by microwave, infrared, and visible sensors for information extraction by novel deconvolution and filtering algorithms

Vladimir Illich Ponomarev; Alejandro Peralta; Ricardo Peralta-Fabi

doi:10.1117/12.197241

21 December 1994 Remote sensing by microwave, infrared, and visible sensors for information extraction by novel deconvolution and filtering algorithms

Vladimir Illich Ponomarev, Alejandro Peralta, Ricardo Peralta-Fabi

Author Affiliations +

Proceedings Volume 2318, Recent Advances in Remote Sensing and Hyperspectral Remote Sensing; (1994) https://doi.org/10.1117/12.197241
Event: Satellite Remote Sensing, 1994, Rome, Italy

Abstract

We present a novel and mathematically sustained procedure for solving the deconvolution- filtering problem. The technique for obtaining a stable solution to the deconvolution (restoration) problem is based on the regularized functional Newman series of the operator equations and gives more precise results than traditional ones. Several novel rank filtering procedures were proposed for decreasing of noise influences. These procedures have given more accuracy and robust results in comparison with other known ones. The efficiency of the techniques proposed has been proven by numerical simulation analysis and by experimental investigations of different kinds of RS objects such as: (1) rural or vegetation covered areas sensed by three microwave frequencies airborne equipment; (2) forest fire areas, industrial installations at night, electrical structures, etc. sensed by infrared and visible sensors; and (3) tropospheric refractive index height profiles restoration by the satellite navigation system `CIKADA' data.

Citation Download Citation

Vladimir Illich Ponomarev, Alejandro Peralta, and Ricardo Peralta-Fabi "Remote sensing by microwave, infrared, and visible sensors for information extraction by novel deconvolution and filtering algorithms", Proc. SPIE 2318, Recent Advances in Remote Sensing and Hyperspectral Remote Sensing, (21 December 1994); https://doi.org/10.1117/12.197241

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