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

IEEE Trans. Signal Process.

Ginolhac

Ovarlez

et al. 2019

This paper explores the problem of change detection in time series of heterogeneous multivariate synthetic aperture radar images. Classical change detection schemes have modelled the data as a realisation of Gaussian random vectors and have derived statistical tests under this assumption. However, when considering high-resolution images, the heterogeneous behaviour of the scatterers is not well described by a Gaussian model. In this paper, the data model is extended to Spherically Invariant Random Vectors where the heterogeneity of the images is accounted for through a deterministic texture parameter. Then three separate detection problems are considered and generalised likelihood ratio test technique is used to derive statistical tests for each problem. The constant false alarm rate property of the new statistics are studied both theoretically and through simulation. Finally, the performance of the new statistics are studied both in simulation and on real synthetic aperture radar data and compared to Gaussian-derived ones. The study yields promising results when the data are heterogeneous.

Section: )mentioning

confidence: 99%

New Robust Statistics for Change Detection in Time Series of Multivariate SAR Images

IEEE Trans. Signal Process.

Ginolhac

Ovarlez

et al. 2019

“…Retrieval of spectro-angular diversity using wavelet decomposition have in peculiar been investigated in work such as [37], [38]. More precisely, these methods have been used for target detection applications in [39] or for change detection in [40]. In those works, the spectro-angular information has presented promising results.…”

Section: B Relation To Prior Workmentioning

confidence: 99%

“…This approach was used in [39], [40] where good results in both target detection and change detection have been obtained. However, in those works, the problem of side lobes has not been considered.…”

Section: B Anisotropy and Dispersivity Of Scatterersmentioning

confidence: 99%

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

IEEE Trans. Geosci. Remote Sensing

Ovarlez

Atto

et al. 2019

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.

“…The number of samples N used, referred as the number of looks, requires to be set by the user as a trade-off between the resolution obtained and performances of estimation. Moreover, this estimation step must be done by taking into account the dimension of the dataset p. Indeed, techniques exploiting time-frequency analysis [9] allow controlling the size of each pixel. Subsequently, the number of looks as well as the size of the vectors determine the performances of any given method of estimation and have to be chosen adequately.…”

Section: Introductionmentioning

confidence: 99%

Designing Sar Images Change-point Estimation Strategies Using an Mse Lower Bound

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Bacharach

Ginolhac

et al. 2019

Self Cite

A growing problem in the remote sensing community concerns the estimation of change-points in a time series of Synthetic Aperture Radar (SAR) images. Although the methodologies of change-point estimation have already been investigated in the literature, there are, to the best of our knowledge, no study on the expected performance for the estimation of change-points in a Wishart distributed time series. This is mainly due to the fact that few results exist on change-point estimation performance in the mathematical literature: the classical central limit theorem does not apply and the classical Cramer-Rao Bound does not exist due to the discrete nature of the parameters. To fill this gap, this paper proposes to use a lower-bound on the Mean Square Error (MSE) with fewer regularity conditions. To this end, recent works on hybrid Cramer-Rao/Weiss-Weinstein bound have been adapted to the specific SAR problematic of interest. Since estimation strategies usually rely on a set of parameters which have to be set by the user, we show how the proposed lower bound allows performing an appropriate tuning. Moreover, the proposed bound is computationally efficient which enables an extensive analysis without a high computational cost.