Filtragem de Projeções Tomográficas da Ciência do Solo Utilizando Kalman Discreto e Redes Neurais (Soil Science Tomographic Projections Filtering using Discret Kalman and Neural Networks)

Marcos Antônio Matos Laia (marcoslaia@gmail.com)1, Paulo Estevão Cruvinel (cruvinel@terra.com.br)2


1Universidade Federal de São Carlos
2EMBRAPA Instrumentação Agropécuária

This paper appears in: Revista IEEE América Latina

Publication Date: March 2008
Volume: 6,   Issue: 1 
ISSN: 1548-0992


Abstract:
In this work the use of Kalman filtering is considered to treat tomographic projections, gotten for the mini-tomographic for Soil science developed by EMBRAPA Instrumentação Agropecuária, disturbed for a time and space variant noise, to get an improvement in the relation Signal/Noise. The results validation was made using ISNR (improvement in signal- noise ratio) as well as the details loss of produced images, which had been generated with the use of the filtered retroprojection algorithm. Is presented a Kalman filtering modification to treat the degraded projections with Poisson noise. To a best ISNR, an Artificial Neural Network had been use in set with the filter.

Index Terms:
adaptive filters, feedforward neural networks, geophysical tomography, kalman filtering, poisson distributions, signal   


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