Dariush Ghorbanzadeh

Bayesian analysis for detecting a change in exponential family

We propose a Bayesian analysis of detection of a change of parameter in a sequence of independent random variables from exponential family. The test uses the highest posterior density credible set.

Detection of random change point in one-parameter exponential families

Abstract We consider a sequence X 1 , X 2 , …, X n of independent random variables which are susceptible to changing their distribution after the [ nT ] first observations where T is a random variable of a distribution known with support in ]0, 1[. The object of this work is to detect the eventual change of distribution—for that—we study the performance of a test based on the statistic of Log-likelihood. It shows that when the number of observations gets larger, the distribution of the statistic Log-likelihood behaves like that of an affine function of the Brownian motion; this allows, by using the concept of contiguity in the sense of LeCam, the evaluation of the asymptotic power function of the test.

Different approaches for the texture classification of a remote sensing image bank

In this paper, we summarize and compare two different approaches used by the authors, to classify different natural textures. The first approach, which is simple and inexpensive in computing time, uses a data bank image and an expert system able to classify different textures from a number of rules established by discipline specialists. The second method uses the same database and a neural networks approach.

Application and Generation of the Univariate Skew Normal Random Variable

In this paper, for the generating the Skew normal random variables, we propose a new method based on the combination of minimum and maximum of two independent normal random variables. The estimation of parameters using the maximum likelihood estimation and the methods of moments estimation method. A real data set has been considered to illustrate the practical utility of the paper.

Various uses of statistical tools for texture analysis

The tools developed by the School of geostatistical have many applications for image segmentation . First, it is very suited to the analysis of natural images eg from remote sensing images and medical images. secondly, they are less expensive in time calculation, as can the methods, from Fourier analysis or matrices coocurrences. We offer reviews of various works of authors to segment natural textures.

Modeling synthetic radar image from a digital terrain model

In this paper we propose to simulate SAR radar images that can be acquired by aircraft or satellite. This corresponds to a real problematic, in fact, an airborne radar data acquisition campaign, was conducted in the south east of France. We want to estimate the geometric deformations that a digital terrain model can be subjected. By extrapolation, this construction should also allow to understand the image distortion if a plane is replaced by a satellite. This manipulation allow to judge the relevance of a space mission to quantify geological and geomorphological data. The radar wave is an electromagnetic wave, they have the advantage of overcoming atmospheric conditions since more wavelength is large is better crossing the cloud layer. Therefore imaging radar provides continuous monitoring.

Construction of Radar SAR Images from Digital Terrain Model and Geometric Corrections

We propose in this paper an original method to correct the geometric distortions of a radar image. The comparison of satellite data, reveals specific problems. Data can be noisy, but especially the geometry of their acquisition requires corrections for comparaisons between them. In this paper we show how highly deformed radar images can be geometrically corrected and compared to map data coming from digital terrain models and also with data coming from SPOT satellite. Radar images used, are from the sensor airborne radar Varan, which is used for data acquisition campaign in the South-East of France. Applications include both structural geology, land cover or study of coastline. We propose a solution to rectify radar image in the geometry of a numerical terrain model. The method adopted here, is to produce a synthesis radar image by encoding all flight parameters of aircraft or satellite from a digital terrain model; radar image can then be compared to the synthetic image because points of landmarks can be clearly identified. Finally, we obtain a correspondence between the points of real radar image distorted, and those in the land or map.