<html><head></head><body><div class="ydp85de053byahoo-style-wrap" style="font-family: Helvetica Neue, Helvetica, Arial, sans-serif; font-size: 13px;"><div dir="ltr" data-setdir="false"><div dir="ltr" style="font-family: Helvetica Neue, Helvetica, Arial, sans-serif;">Prezados redistas, o Programa de Pós-Graduação em Matemática da Universidade Federal do Amazonas (PPGM-UFAM) convida a todos para o seminário a ser realizado no dia 02 de dezembro de 2021 às 10h (horário de Brasília).</div><div dir="ltr" style="font-family: Helvetica Neue, Helvetica, Arial, sans-serif;"><span style="color: rgb(0, 0, 0);"><br></span></div><div dir="ltr" style="font-family: Helvetica Neue, Helvetica, Arial, sans-serif;"><div dir="ltr" data-setdir="false"><b>Palestrante</b>: Profa.<span> Dra. Bruna Gregory Palm (Blekinge Institute of Technology - Suécia).</span></div></div><div dir="ltr" style="font-family: Helvetica Neue, Helvetica, Arial, sans-serif;"><br></div><div style="font-family: Helvetica Neue, Helvetica, Arial, sans-serif;"><div dir="ltr"><div><div dir="ltr" data-setdir="false"><b>Título</b>: Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers<div><div><br></div></div><div><br></div></div></div><div dir="ltr"><b>Abstract</b>: The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This paper aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the non-robust estimators show a relative bias value 65-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about 96% and 10%, respectively, in the mean absolute value of both measures, in compassion to the non-robust estimators. Moreover, two SAR data sets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature.</div><div dir="ltr" data-setdir="false"><div><div><br></div></div><br></div></div></div></div><div dir="ltr" data-setdir="false"><br></div><div dir="ltr" data-setdir="false">Link Youtube: <div><div>https://www.youtube.com/playlist?list=PLQaCvpL1S-z79j-UEdnJARrvIWGcuc5UG</div><div><br></div></div></div><div dir="ltr" data-setdir="false"><br></div><div><br></div><div class="ydp85de053bsignature"><div style="font-family:Helvetica, Arial, sans-serif;font-size:13px;"><div>Atenciosamente,<br><br><span style="font-family:arial, helvetica, sans-serif;">Jhonnata Bezerra de Carvalho</span></div><div><span style="font-family:arial, helvetica, sans-serif;">Doutor em Estatística - PPGE - UFPE<br></span>Mestre em Matemática Aplicada e Estatística - PPgMAE - UFRN<br><span style="font-family:arial, helvetica, sans-serif;">Graduado em Estatística - UFRN</span><br>----------------------------<br></div></div></div></div></body></html>