New SAR Algorithm Based on Orthogonal Projections for MMT Detection and Interference Reduction

Brigui, Frédéric; Thirion-Lefevre, Laetitia; Ginolhac, Guillaume; Forster, Philippe

doi:10.1109/tgrs.2013.2276417

Cited by 9 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the width and/or the type of pipe could be integrated into the steering vector by using the subspace approaches of [21], [22] and [23]. …”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Robust adaptive detection of buried pipes using GPR

et al. 2017

View full text Add to dashboard Cite

Detection of buried objects such as pipes using a Ground Penetrating Radar (GPR) is intricate for three main reasons. First, noise is important in the resulting image because of the presence of several rocks and/or layers in the ground, highly influencing the Probability of False Alarm (PFA) level. Also, wave speed and object responses are unknown in the ground and depend on the relative permittivity, which is not directly measurable. Finally, the depth of the pipes leads to strong attenuation of the echoed signal, leading to poor SNR scenarios. In this paper, we propose a detection method: (1) enhancing the signal of interest while reducing the noise and layer contributions, and (2) giving a local estimate of the relative permittivity. We derive an adaptive detector where the signal of interest is parametrised by the wave speed in the ground. For this detector, noise is assumed to follow a Spherically Invariant Random Vector (SIRV) distribution in order to obtain a robust detection. We use robust maximum likelihood-type covariance matrix estimators called M-estimators. To handle the significant amount of data, we consider regularised versions of said estimators. Simulation will allow to estimate the relation PFA-Threshold. Comparison is performed with standard GPR processing methods, showing the aptitude of the method in detecting pipes having low response levels with a reasonable PFA.

show abstract

“…For example, the width and/or the type of pipe could be integrated into the steering vector by using the subspace approaches of [21], [22] and [23]. …”

Section: Resultsmentioning

confidence: 99%

“…The proposed detector depends on a signal of interest, this being the theoretical response of a buried pipe. This signal is concatenated in a vector denoted steering vector by analogy with existing work in the SAR domain [21], [22], [23]. This steering vector is built from the theoretical hyperbolas and the known transmitted signal.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive detection of buried pipes using GPR

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Several work have investigated methods for retrieving the spectro-angular diversity. For example, approach such as steerable pyramids [24], [25], curvelets [26] or subspaces [27], [28] are possible. However, they are usually heavy methods.…”

Section: B Relation To Prior Workmentioning

confidence: 99%

Design of New Wavelet Packets Adapted to High-Resolution SAR Images With an Application to Target Detection

Mian

Ovarlez

Atto

et al. 2019

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

High-Resolution in synthetic aperture radar (SAR) leads to new physical characterizations of scatterers which are anisotropic and dispersive. These behaviors present an interesting source of diversity for target detection schemes. Unfortunately, such characteristics have been integrated and have been naturally lost in monovariate single look SAR images. Modeling this behavior as non-stationarity, wavelet analysis has been successful in retrieving this information. However, the sharp-edge of the used wavelet functions introduces undesired high side-lobes for the strong scatterers present in the images. In this paper, a new family of parametrized wavelets, designed specifically to reduce those side-lobes in the SAR image decomposition, is proposed. Target detection schemes are then explored using this spectroangular diversity and it can be shown that in High-Resolution SAR images, the non-Gaussian and robust framework leads to better results.

show abstract

“…We assume that the scatterers present in the scene will respond to an electromagnetic wave in all directions and in all frequencies. This hypothesis is reasonable for low-resolution SAR systems, but it was shown ( [7], [8], [9], [10], [11], [12]) that for recent high-resolution SAR imagery, it does no longer apply. The scatterers can have a peculiar signature in angle and frequency domains.…”

Section: Introductionmentioning

confidence: 99%

Multivariate change detection on high resolution monovariate SAR image using linear time-frequency analysis

Mian

Ovarlez

Ginolhac

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-In this paper, we propose a novel methodology for Change Detection between two monovariate complex SAR images. Linear Time-Frequency tools are used in order to recover a spectral and angular diversity of the scatterers present in the scene. This diversity is used in bi-date change detection framework to develop a detector, whose performances are better than the classic detector on monovariate SAR images.

show abstract

New SAR Algorithm Based on Orthogonal Projections for MMT Detection and Interference Reduction

Cited by 9 publications

References 19 publications

Robust adaptive detection of buried pipes using GPR

Robust adaptive detection of buried pipes using GPR

Design of New Wavelet Packets Adapted to High-Resolution SAR Images With an Application to Target Detection

Multivariate change detection on high resolution monovariate SAR image using linear time-frequency analysis

Contact Info

Product

Resources

About