&lt;title&gt;Development of a digital videogrammetric airborne system&lt;/title&gt;

Peralta-Fabi, Ricardo; Peralta, Alejandro; Prado, Jorge Moisés Kroll do; Navarrete, M.; Portillo, Wendy

doi:10.1117/12.157150

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“…The above mentioned procedure is based on the following system of In this case, except for two kinds of errors connected with the structure of E -the dispersion of the dynamic part is given by: (5) and noise N that is influenced by dispersion aEN '2[a +'v]2 (6) the part connected with disturbances 5S, 5RN, öRE has been found by the dispersion a2 2(a+fl2a1 (7) All equations (5-7) depend on the spectral filter function i'(cK ) to decrease errors in the E used, the Fourier transforms:…”

Section: -Introductionmentioning

confidence: 99%

“…With the help of these algorithms it is possible to obtain predicted, precise and robust characteristics for the image processed when considering different degrees of quality of the a priori information about the signal and noise probability distributions. 226 By means of such methods and algorithms various tipes of sensing, in the microwave (MW), infrared (IR) and visible (V) bands, several objects have been investigated, such as: a) Rural or vegetation covered areas sensed by MW airborne equipment (wavelengths X= 8mm, 3 cm and 23 cm); b) Forest fire areas, industrial plants during the night shift, electrical power structures and other installations sensed by JR (8 to l2jzm) and V airborne equipment; c) Tropospheric height profile derived from the refraction index evaluation through satellite data from the navigation system 'CIKADA" (6)(7)(8) …”

Section: -Introductionmentioning

confidence: 99%

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

Пономарев

Peralta²,

Peralta-Fabi³

1994

SPIE Proceedings

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We present a novel and mathematically sustained procedures for solving deconvolution-filtering problem.The technique for obtaining a stable solution to the deconvolution (restoration) problem is based on the regularized functional Newman serias of the operator equations and gives more precise results than traditional ones.It was proposed several novel rank filtering procedures for decreasing of noise influences. These procedures have given more accuracy and robust results in comparison with known ones.The efficiency of the techniques proposed hove been proved by numerical simulation analysis and by experimental investigations of different kinds of RS objects:. rural or vegetation covered areas sensed by three microwave frequencies airborne equipment. . forest fire areas, industrial installations at night, electrical structures, etc. sensed by infrared and visible sensors;. tropospheric refractive index height profiles restoration by satellite navigation system "CIKADA" data. 1.-INTRODUCTIONIn different remote sensing (RS) experiments the efficiency in data collection depends upon the physical properties, as well as the a priori information of the object observed. For realizing the possibility of extracting information a modern RS system uses either a multispectral or a multitemporal observation strategy from both air and spaceborne platforms" 2, 5, As a rule image data is degraded by multiplicative and additive noises (Gaussian, impulse, etc.) and by different kinds of inaccuracies resulting from sensor and atmospheric influences.The resolution of the images is dictated by the need to solve the restoration problem by means of an inverse operator (usually integral) equation with the kernel depending on the sensors used. Any restoration procedure for image improvement requires the solution of an iliposed inverse problem. Thus, uncertainty arises from small perturbation in the input RS data, that in turn give rise to considerable errors on the restored image parameters.Different types of algorithms have been proposed for the deconvolution (restoration) problem in RS and other image processing applications" 2, 4, 5, 6 Except for this, the influence of diverse noises and inaccuracies dictate a certain filtering procedure in order to increase the quality of the RS information.In this paper we present a novel and a mathematically sustained procedure for solving deconvolutionfiltering problems.The technique for obtaining a stable solution to the deconvolution problem is based on the regularized functional Newman series of the operator equations which produce more precise and robust results than traditional solutions.We propose several new rank filtering algorithms for decreasing noise influences. With the help of these algorithms it is possible to obtain predicted, precise and robust characteristics for the image processed when considering different degrees of quality of the a priori information about the signal and noise probability distributions. 226 ISPIE Vol. 2318 Q-819416487/94/$6.OO Downloaded From: http://proceedi...

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Section: -Introductionmentioning

confidence: 99%

Section: -Introductionmentioning

confidence: 99%