Oscar H. Bustos

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The GA0 distribution is assumed as the universal model for multilook amplitude SAR imagery data under the Multiplicative Model. This distribution has two unknown parameters related to the roughness and the scale of the signal, that can be used in image analysis and processing. It can be seen that Maximum Likelihood and moment estimators for its parameters(More)
This paper presents two new MAP (Maximum a Posteriori) filters for speckle noise reduction and a Monte Carlo procedure for the assessment of their performance. In order to quantitatively evaluate the results obtained using these new filters, with respect to classical ones, a Monte Carlo extension of Lee’s protocol is proposed. This extension of the protocol(More)
En este trabajo se considera el problema de estimar la rugosidad de blancos sensados con radar de apertura sintética – SAR, bajo la hipótesis del Modelo Multiplicativo para datos en formato de amplitud, una o múltiples vistas y muestras de tamaño moderado. Se supone que los datos obedecen una distribución muy flexible, recientemente propuesta para la(More)
This work presents the results of assessing the accuracy of statistical routines as implemented in the IDL platform, versions 5.6 and 6.0 for Windows XP and Linux. This is “a complete computing environment for the interactive analysis and visualization of data. IDL integrates a powerful, array-oriented language with numerous mathematical analysis and(More)
This paper presents a general result for the simulation of correlated heterogeneous targets, which are present in images corrupted by speckle noise. This technique is based on the use of a correlation mask and Gaussian random variables, in order to obtain spatially dependent Gamma deviates. These Gamma random variables, in turn, allow the obtainment of(More)
Imputation of missing data in large regions of satellite imagery is necessary when the acquired image has been damaged by shadows due to clouds, or information gaps produced by sensor failure. The general approach for imputation of missing data, that could not be considered missed at random, suggests the use of other available data. Previous work, like(More)
We present the assessment of two classification procedures using a Monte Carlo experience and Landsat data. Classification performance is hard to assess with generality due to the huge number of variables involved. In this case, we consider the problem of classifying multispectral optical imagery with pointwise Gaussian maximum likelihood and contextual ICM(More)