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

Denoising RENOIR Image Dataset with DBSR

Noise reduction algorithms have often been evaluated using images degraded by artificially synthesised noise. The RENOIR image dataset [3] provides an alternative way for testing noise reduction algorithms on real noisy images and we propose in this paper to assess our CNN called De-Blurring Super-Resolution (DBSR) [2] to reduce the natural noise due to low light conditions in a RENOIR dataset

Synthetic Aperture Radar Image Classification via Mixture Approaches

International audienceIn this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors

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

Modeling the statistics of high resolution SAR images

In 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 modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants 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). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (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 measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images

Punching shear strength under static and dynamic loads

Modern domestic calculation methods and developed countries for determining the bearing capacity of monolithic reinforced concrete slabs for punching do not fully take into account all factors of design solutions and operating conditions. The available design provisions are made for the static operation of structures and there are no recommendations for taking into account the features of the dynamic impact on the overlap and the nature of the work of the node interfaces. The accepted empirical assumptions of the calculation, based on numerous experimental data, do not take into account the features of the stress-strain state of the coupling of the overlap with the column during destruction according to the punching scheme. This is due to the lack of computational models in which all the acting internal forces ensuring the resistance of the interface to penetration would be considered comprehensively. The complexity of the problem is due to the fact that the sections of the nodal interface are in an inhomogeneous stressed state. The stress-strain state of plates for punching under dynamic load is currently little studied. This article proposes a method for determining the bearing capacity of a symmetrical nodal coupling of a column with an overlap for punching under static and short-term dynamic loading. The proposed design model of the punching strength is based on the following prerequisites: the resistance to punching of a monolithic reinforced floor consists of the shear resistance along the surface of the reduced punching pyramid formed by the height of the compressed concrete zone; the strength of the concrete shear resistance increases due to volumetric compressive forces on the surface of the reduced punching pyramid; the angle of inclination of the faces of the punching pyramid depends on the loading speed. The obtained theoretical dependences are applicable under static and dynamic loading and are in satisfactory agreement with experimental data