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Table Of Integrals Series And Products

On robust estimation of the location parameter

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Mathematical Methods Of Statistics

An explicit form of the linear multichannel synthetic aperture radar (SAR) intensity filter, which preserves radiometry while optimally reducing speckle is derived, together with a compact expression for the theoretical gain in equivalent numbers of looks (ENLs). The filter can be applied to mixed data types, which is demonstrated using a combination of ERS and JERS satellite data, and confirms the filter performance predicted by the theory. Tests indicate that a simplified form of the filter, which neglects correlation between images, gives an ENL only slightly less than optimal, while being much easier to implement. Exact analysis of the effect of estimating filter weights shows that the linear increase in ENL with the number of images predicted for the ideal filter does not occur. In practice, the ENL is affected by the window size used to estimate the weights and saturates as the number of images increases. An efficient recursive form of the filter is described, which is most naturally applied to multitemporal data for the practically important case where the current image is uncorrelated with previous images in a data sequence.

Filtering of multichannel SAR images

Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a “superimage” by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the “superimage”; and 3) computation of the denoised image by remultiplying the denoised ratio by the “superimage.” Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the “superimage” that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (SAR) time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.

/pdf/ratio-based-multitemporal-sar-images-denoising-rabasar-3pftg618zc.pdf

Ratio-Based Multitemporal SAR Images Denoising: RABASAR

https://hal.archives-ouvertes.fr/hal-01375659/document

Robust adaptive detection of buried pipes using GPR

This paper presents a new method based on the curvelet transform to improve the readability of ground penetrating radar (GPR) data during localization works of buried pipes. The localization is achieved by the detection of hyperbolas in the GPR cross section, also called B-scan. However, GPR antenna collects abundant information from the ground, which can partially hide useful information, especially echoes from the pipes (i.e., hyperbolas). In order to highlight these echoes, it is important to reduce noise and to remove undesirable items such as the clutter and column artifacts. For this purpose, we propose to use the curvelet transform in a three processing step method: clutter, noise, and column artifact removal. Thanks to prior information on the distribution of the coefficients and on the orientation of the clutter and artifacts, we are able to reduce them in the B-scan. These steps can be used independently giving more flexibility to the operator. Moreover, the processing is fast to compute and requires only one easily tunable parameter. This is appropriate for a computation in real-time by nonexpert operators. Our method has been applied to real data acquired on a test area and under real conditions. We show the proposed method presents satisfying qualitative and quantitative results compared to other methods.

Application of the Curvelet Transform for Clutter and Noise Removal in GPR Data

The aim of this paper is to compare the potential of two popular flexible laws, the Fisher distribution and the Generalized Gamma distribution, for the statistical modeling of high-resolution SAR data through an original “mimicking-based” approach The presented study allows to evaluate the ability of both laws to correctly imitate or “mimic” another reference law, frequently used for modeling the intensity of SAR images and chosen for instance as the $\mathcal {K}$ law or the Weibull, Beta or log-normal laws in this paper This study uses log-cumulant statistics for parameter estimation of the imitating law and involves quantitative criteria of comparison based on the Kullback–Leibler divergences between the reference law and the Fisher law or the Generalized Gamma law The mimicking capacities of both distributions are first analyzed for some sets of parameters describing different studied cases, covering a wide set of possible mimicked reference laws The high modeling potential of both distributions is then illustrated on heterogeneous subscenes from real SAR intensity data Pragmatical considerations are also taken into account to draw up recommendations about the preferential use of a distribution and to highlight complementarities of both Fisher and Generalized Gamma distributions, along with limitations of the approach

Mimic Capacity Of Fisher And Generalized Gamma Distributions For High-Resolution SAR Image Statistical Modeling

In order to improve asset knowledge and avoid third part damages during road works, the localization of gas pipes in a non-destructive way has become a wide domain of research during these last years. Several devices have been developed in order to answer this problem. Acoustic, electromagnetic or RFID technologies are used to find pipes in the underground. Ground Penetrating Radar (GPR) is also used to detect buried gas pipes. However it does not directly provide a 3D position but a reflection map called B-scan that the user must interpret. In this paper, we propose a novel method to automatically get the position of gas pipes with GPR acquisitions. This method uses a dictionary of theoretical pipe signatures. The correlation between each atom from the dictionary and the B-scan is used as feature in a two part supervised learning scheme. Our method has been applied to real data acquired on a test area and in real condition. The proposed method presents satisfying qualitative and quantitative results compared to other methods.

/pdf/automatic-localization-of-gas-pipes-from-gpr-imagery-1ac60aeoxb.pdf

Jean-Marie Nicolas

Papers

Ratio-Based Multitemporal SAR Images Denoising: RABASAR

Robust adaptive detection of buried pipes using GPR

Application of the Curvelet Transform for Clutter and Noise Removal in GPR Data

Mimic Capacity Of Fisher And Generalized Gamma Distributions For High-Resolution SAR Image Statistical Modeling

Automatic localization of gas pipes from GPR imagery