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DTSTART:20170312T030000
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BEGIN:VEVENT
DTSTAMP:20171211T204910Z
UID:F421C05C-E5B6-11E7-833E-0050568D7F66
DTSTART;TZID=America/New_York:20170714T100000
DTEND;TZID=America/New_York:20170714T110000
DESCRIPTION:Statistics has a prominent role in SAR - Synthetic Aperture Rad
 ar image processing and analysis. More often than not\, these data cannot 
 be described by the usual additive Gaussian noise model. Rather than that\
 , a multiplicative signal-dependent model adequately describes the observa
 tions.\n\nAfter summarizing the main distributions for both the univariate
  (intensity and amplitude) and multivariate (fully polarimetric) image for
 mats\, we present eight seemingly different problems\, how they can be for
 mulated and solved in a unified manner. The approach is based on Informati
 on Theory\, through stochastic distances between models.\n\nQuebec\, Quebe
 c\, Canada
LOCATION:Quebec\, Quebec\, Canada
ORGANIZER:Georges.Fournier@drdc-rddc.gc.ca
SEQUENCE:0
SUMMARY:[Legacy Report] Statistics for SAR Image Analysis 
URL;VALUE=URI:https://events.vtools.ieee.org/m/154916
X-ALT-DESC:Description: <br /><p>Statistics has a prominent role in SAR - S
 ynthetic Aperture Radar image processing and analysis. More often than not
 \, these data cannot be described by the usual additive Gaussian noise mod
 el. Rather than that\, a multiplicative signal-dependent model adequately 
 describes the observations.</p>\n<p>After summarizing the main distributio
 ns for both the univariate (intensity and amplitude) and multivariate (ful
 ly polarimetric) image formats\, we present eight seemingly different prob
 lems\, how they can be formulated and solved in a unified manner. The appr
 oach is based on Information Theory\, through stochastic distances between
  models.</p>
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