Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields

International audienceIn this paper we develop a supervised classification approach for medium and high resolution multichannel synthetic aperture radar (SAR) amplitude images. The proposed technique combines finite mixture modeling for probability density function estimation, copulas for multivariate distribution modeling and a Markov random field (MRF) approach to Bayesian classification. The novelty of this research is in introduction of copulas to classification of D-channel SAR, with D>2, within the mainframe of finite mixtures - MRF approach. This generalization results in a flexible and well performing multichannel SAR classification technique. Its accuracy is validated on several multichannel Quad-pol RADARSAT-2 images and compared to benchmark classification techniques

High resolution SAR-image classification

In this report we propose a novel classification algorithm for high and very high resolution synthetic aperture radar (SAR) amplitude images that combines the Markov random field approach to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done by dictionary-based stochastic expectation maximization amplitude histogram estimation approach. The developed semiautomatic algorithm is extended to an important case of multi-polarized SAR by modeling the joint distributions of channels via copulas. The accuracy of the proposed algorithm is validated for the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed

Resultados de la recaudación del impuesto a las nóminas en el caribe mexicano: gestión presidencial 2018 - 2019

Analysis has been made on whether the presidential administration of Mexico, which began in December 2018 with anti-neoliberal economic policies, has had an impact and to what extent whit respect to the collection of the payroll tax in the state of Quintana Roo and, as a contrast variable, the international passengers.  The adjustment variable, identify the distribution in order to represent the magnitude of the changes. To find out if there is some independence from the government management, it´s been realized independence test in different presidential terms. It is note that there were significant changes in the collection of the payroll test, however, the Independence test suggest that despite them where unrelated to the presidential management.Se analiza si la administración presidencial de México, iniciada en diciembre 2018, con políticas económicas antineoliberales, ha repercutido y en qué magnitud respecto a la recaudación del impuesto sobre nóminas en el estado de Quintana Roo y, como variable de contraste, en los pasajeros internacionales. Mediante pruebas de ajuste, se identifica la distribución para representar la magnitud de los cambios. Para conocer si existe independencia de la gestión gubernamental, se realizan pruebas de independencia en períodos presidenciales distintos. Se advierte que en la recaudación sí se presentaron cambios significativos, no obstante, la prueba de independencia sugiere que a pesar de ello fueron ajenos a la gestión presidencial

Unsupervised amplitude and texture based classification of SAR images with multinomial latent model

We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images for classification purpose. We use Nakagami density to model the class amplitudes and a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error to model the textures of the classes. A non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We obtained some classification results of water, land and urban areas in both supervised and unsupervised cases on TerraSAR-X, as well as COSMO-SkyMed data

High resolution SAR-image classification

In this report we propose a novel classification algorithm for high and very high resolution synthetic aperture radar (SAR) amplitude images that combines the Markov random field approach to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done by dictionary-based stochastic expectation maximization amplitude histogram estimation approach. The developed semiautomatic algorithm is extended to an important case of multi-polarized SAR by modeling the joint distributions of channels via copulas. The accuracy of the proposed algorithm is validated for the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed

Unsupervised amplitude and texture classification of SAR images with multinomial latent model

International audienceWe combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images for modelbased classification purpose. In a finite mixture model, we bring together the Nakagami densities to model the class amplitudes and a 2D Auto-Regressive texture model with t-distributed regression error to model the textures of the classes. A nonstationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We present our results on the classification of the land covers obtained in both supervised and unsupervised cases processing TerraSAR-X, as well as COSMO-SkyMed data

Dictionary-based probability density function estimation for high-resolution SAR data

Copyright 2009 by SPIE and IS\&T. This paper was published in the proceedings of IS\&T/SPIE Electronic Imaging 2009 Conference in San Jose, USA, and is made available as an electronic reprint with permission of SPIE and IS\&T. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.International audienceIn the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is an extension of previously existing method for lower resolution images. The method integrates the stochastic expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). The proposed dictionary consists of eight state-of-the-art SAR- specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and the algorithm to automatically estimate the optimal number of mixture components. The experimental results with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive accuracy measures such as correlation coefficient (above 99,5%). The method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes