Statistical modelling of ocean SAR images

Delignon, Yves; Garello, R.; Hillion, A.

doi:10.1049/ip-rsn:19971497

Cited by 45 publications

(27 citation statements)

References 10 publications

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“…The goal of these studies was the modeling of either land or sea areas [4][5][6][7][8][9]. However, the goal of this paper is not the modeling of sea areas, but the classification of sea states using the information given by the statistical distributions and the impact the speckle has on sea clutter distribution.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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“…We assume that the active sensor property is modelled as a multiplicative stationary random noise field n(x, y ) which is independent of the signal t(x, y ) [7]. Therefore, the image field s(x, y) is generated by the algebraic product of image fields t(x, y ) and n(x, y ) according to the simple relation S(X?Y> = t ( X , Y ) * n ( x , y )…”

Section: Multiplicative Noisementioning

confidence: 99%

An optimal matched filter for target detection in images distorted by noise

Benyonssef¹,

Delignon²,

Ghorbel³

1998

IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings.

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To analyse automatically remote sensing images, many pattern recognition methods have been developed. Among the former, quote the segmentation that consists in recognition of textures in images or again the target detection whose goal is to find a reference target in an observation sensor. This last is the object of this paper. We define a detection algorithm independent of the position of the sensor as compared to the target.. We investigate how the optimal filter depend on the transformations, the prototype pattern and sensor properties, and we will give explicit examples of optimal filter functions for active and passive sensor.

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“…This can be useful when backscatter results on the expected densities are known a priori. One interesting point is also to test the specific SAR-oriented KUBW system described in [12] in the context of sea-ice image.…”

Section: Discussionmentioning

confidence: 99%

Estimation of sea-ice SAR clutter statistics from Pearson's system of distributions

Derrode

Mercier²,

Caillec³

et al.

IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing

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SAR images can be used to help ship routing in sea-ice conditions. In this study, we focus on the Antarctic region where no multi-year ice nor big ice floes are to be found. As a matter of fact, each clutter obeys to a backscattering mechanism that induces a specific pixel distribution and our attempt is to identify automatically the correct distribution for each ice type. The problem is that of generalized mixture estimation and unsupervised image classification. In this work, we modelled the mixture with distributions from the Pearson's system. Parameters estimation is realized according to the ICE algorithm in the context of hidden Markov chains. The results obtained from the Pearson's system are compared to the ones obtained with a classical mixture of Gaussian distributions.

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Statistical modelling of ocean SAR images

Cited by 45 publications

References 10 publications

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Statistical Analysis of SAR Sea Clutter for Classification Purposes

An optimal matched filter for target detection in images distorted by noise

Estimation of sea-ice SAR clutter statistics from Pearson's system of distributions

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