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SEAFLOOR CLASSIFICATION USING STATISTICAL MODELING OF WAVELET SUBBANDS

N.-E. Lasmar 1 A. Baussard 2 Gilles Le Chenadec 3
1 REMS
STIC - Département STIC [Brest]
2 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_ENSTAB_CID_SFIIS ; OSM
STIC - Département STIC [Brest], Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : This paper deals with the classification of textured seafloor images recorded by sidescan sonar. To address this problem, a supervised classification approach based on the Bayesian framework is proposed. In this way, the textured images are characterized through parametric probabilistic models of the wavelet coefficients. The generalized Gaussian distribution (GGD), which is a well-established model to characterize the marginal distributions of the wavelet subbands, is considered. However, to take into account the joint statistics of wavelet coefficients, we also consider the Gaussian copula based multivariate generalized Gaussian model (GC-MGG). A supervised learning context is adopted for the classification stage by using a probabilistic k-Nearest Neighbors classifier. Each textured image will be represented by its GGD or GC-MGG estimated parameters and given a collection of training images the Kullback-Leibler divergence is used to estimate the similarity between a test image and seafloor classes. Experiments on real sonar textured images are proposed to highlight the interest of this approach
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https://hal.archives-ouvertes.fr/hal-01171474
Contributor : Annick Billon-Coat <>
Submitted on : Friday, July 3, 2015 - 5:11:23 PM
Last modification on : Wednesday, April 21, 2021 - 11:36:03 AM

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  • HAL Id : hal-01171474, version 1

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N.-E. Lasmar, A. Baussard, Gilles Le Chenadec. SEAFLOOR CLASSIFICATION USING STATISTICAL MODELING OF WAVELET SUBBANDS. UA 2014, Jun 2014, Rhodes, Greece. ⟨hal-01171474⟩

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