Change detection in multisensor SAR images using bivariate gamma distributions

This paper studies a family of distributions constructed from multivariate gamma distributions to model the statistical properties of multisensor synthetic aperture radar (SAR) images. These distributions referred to as multisensor multivariate gamma distributions (MuMGDs) are potentially interesting for detecting changes in SAR images acquired by different sensors having different numbers of looks. The first part of the paper compares different estimators for the parameters of MuMGDs. These estimators are based on the maximum likelihood principle, the method of inference function for margins and the method of moments. The second part of the paper studies change detection algorithms based on the estimated correlation coefficient of MuMGDs. Simulation results conducted on synthetic and real data illustrate the performance of these change detectors

Ionization Correction Factors for Planetary Nebulae: I- Using optical spectra

We compute a large grid of photoionization models that covers a wide range of physical parameters and is representative of most of the observed PNe. Using this grid, we derive new formulae for the ionization correction factors (ICFs) of He, O, N, Ne, S, Ar, Cl, and C. Analytical expressions to estimate the uncertainties arising from our ICFs are also provided. This should be useful since these uncertainties are usually not considered when estimating the error bars in element abundances. Our ICFs are valid over a variety of assumptions such as the input metallicities, the spectral energy distribution of the ionizing source, the gas distribution, or the presence of dust grains. Besides, the ICFs are adequate both for large aperture observations and for pencil-beam observations in the central zones of the nebulae. We test our ICFs on a large sample of observed PNe that extends as far as possible in ionization, central star temperature, and metallicity, by checking that the Ne/O, S/O, Ar/O, and Cl/O ratios show no trend with the degree of ionization. Our ICFs lead to significant differences in the derived abundance ratios as compared with previous determinations, especially for N/O, Ne/O, and Ar/O.Comment: 19 pages, 22 figures. Accepted for publication in MNRA

Chlorine and Sulfur in Nearby Planetary Nebulae and H II Regions

We derive the chlorine abundances in a sample of nearby planetary nebulae (PNe) and H II regions that have some of the best available spectra. We use a nearly homogeneous procedure to derive the abundance in each object and find that the Cl/H abundance ratio shows similar values in H II regions and PNe. This supports our previous interpretation that the underabundance we found for oxygen in the H II regions is due to the depletion of their oxygen atoms into organic refractory dust components. For other elements, the bias introduced by ionization correction factors in their derived abundances can be very important, as we illustrate here for sulfur using photoionization models. Even for low-ionization PNe, the derived sulfur abundances can be lower than the real ones by up to 0.3 dex, and the differences found with the abundances derived for H II regions that have similar S/H can reach 0.4 dex.Comment: 2 pages, 1 figure, proceedings of the IAU Symposium No. 283, Planetary Nebulae: an Eye to the Futur

Bivariate Gamma Distributions for Image Registration and Change Detection

This paper evaluates the potential interest of using bivariate gamma distributions for image registration and change detection. The first part of this paper studies estimators for the parameters of bivariate gamma distributions based on the maximum likelihood principle and the method of moments. The performance of both methods are compared in terms of estimated mean square errors and theoretical asymptotic variances. The mutual information is a classical similarity measure which can be used for image registration or change detection. The second part of the paper studies some properties of the mutual information for bivariate Gamma distributions. Image registration and change detection techniques based on bivariate gamma distributions are finally investigated. Simulation results conducted on synthetic and real data are very encouraging. Bivariate gamma distributions are good candidates allowing us to develop new image registration algorithms and new change detectors