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Higher-Order Statistics for the Detection of Small Objects in a Noisy Background Application on Sonar Imaging

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 047039 (2007) Cite this article

Abstract

An original algorithm for the detection of small objects in a noisy background is proposed. Its application to underwater objects detection by sonar imaging is addressed. This new method is based on the use of higher-order statistics (HOS) that are locally estimated on the images. The proposed algorithm is divided into two steps. In a first step, HOS (skewness and kurtosis) are estimated locally using a square sliding computation window. Small deterministic objects have different statistical properties from the background they are thus highlighted. The influence of the signal-to-noise ratio (SNR) on the results is studied in the case of Gaussian noise. Mathematical expressions of the estimators and of the expected performances are derived and are experimentally confirmed. In a second step, the results are focused by a matched filter using a theoretical model. This enables the precise localization of the regions of interest. The proposed method generalizes to other statistical distributions and we derive the theoretical expressions of the HOS estimators in the case of a Weibull distribution (both when only noise is present or when a small deterministic object is present within the filtering window). This enables the application of the proposed technique to the processing of synthetic aperture sonar data containing underwater mines whose echoes have to be detected and located. Results on real data sets are presented and quantitatively evaluated using receiver operating characteristic (ROC) curves.

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Authors and Affiliations

  1. Groupe d'Electromagnétisme Appliqué (GEA), PST Ville d'Avray, Université Paris X-Nanterre, 1 chemin Desvallières, Ville d'Avray, 92410, France

    F. Maussang

  2. Laboratoire des images et des signaux (LIS) GIPSA, École Nationale Supérieure d'Ingénieurs Électriciens de Grenoble, Institut National Polytechnique de Grenoble (INPG), Domaine Universitaire, BP 46, Saint-Martin-d'Hères Cedex, 38402, France

    J. Chanussot

  3. Groupe d'Etudes Sous-Marines de l'Atlantique, DGA/DET/GESMA, BP 42, Brest Armées, 29240, France

    A. Hétet & M. Amate

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  1. F. Maussang
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Correspondence to F. Maussang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Maussang, F., Chanussot, J., Hétet, A. et al. Higher-Order Statistics for the Detection of Small Objects in a Noisy Background Application on Sonar Imaging. EURASIP J. Adv. Signal Process. 2007, 047039 (2007). https://doi.org/10.1155/2007/47039

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