Marine observations with a harmonic single-beam echo-sounder

International audienceTo characterise the seabed or water-column targets with acoustics, it is common to use multiple frequencies and therefore several sonar transducers or echo-sounders. The single beam echo-sounder we present here is able, thanks to non-linearity of the sea water, to generate more than three harmonics above its fundamental transmitted frequency, in effect producing four distinct frequencies with a single echo-sounder. In addition, all transmitted signals are perfectly in phase because they are carried by the same pulse, which has obvious benefits for further processing of the echoes. In this presentation, after a short review of the entire system, its application to seabed characterisation using the reflectivity level (acoustic backscattering strength from the seafloor) will be exposed. Further developments of plans to use this echo-sounder for fishery acoustics will then be highlighted, based on datasets acquired in the Bay of Brest (France). (Project funded by ANR and DGA / ANR-14-ASTR-0022-00)

Comparison of methods employed to extract information contained in seafloor backscatter

International audienceSeabed maps are based on quantities extracted from measurements of the seafloor‘s acoustic response by sonar systems such as single-beam echo-sounders (SBES), multibeam echo-sounders (MBES) or sidescan sonars (SSS). In this paper, a comparison of various strategies to estimate the backscattering strength (BS) from recorded time-series, i.e. seabed echoes extracted from pings, is presented. The work hypotheses are based on processed data from a SBES designed to be tilted mechanically. Ideal survey conditions are taken into account and the seafloor is supposed to be rough so that BS is assumed to be equivalent to the Rayleigh probability density function parameter. Classical methods such as averaging corrected (sonar equation) backscattered single values over a set of pings to estimate BS are compared to other methods exploiting several time-samples being part of pings. Simulated data is considered to estimate BS in different situations (several estimators, natural/squared values, number of samples and pings). The best estimator to reach a 0.1dB uncertainty is proposed, and a formula governing the number of time-samples and pings needed to reach an accurate BS estimation according to the measurement conditions is derived

Simultaneous Control and Guidance of an AUV Based on Soft Actor–Critic

International audienceThe marine environment is a hostile setting for robotics. It is strongly unstructured, uncertain, and includes many external disturbances that cannot be easily predicted or modeled. In this work, we attempt to control an autonomous underwater vehicle (AUV) to perform a waypoint tracking task, using a machine learning-based controller. There has been great progress in machine learning (in many different domains) in recent years; in the subfield of deep reinforcement learning, several algorithms suitable for the continuous control of dynamical systems have been designed. We implemented the soft actor–critic (SAC) algorithm, an entropy-regularized deep reinforcement learning algorithm that allows fulfilling a learning task and encourages the exploration of the environment simultaneously. We compared a SAC-based controller with a proportional integral derivative (PID) controller on a waypoint tracking task using specific performance metrics. All tests were simulated via the UUV simulator. We applied these two controllers to the RexROV 2, a six degrees of freedom cube-shaped remotely operated underwater Vehicle (ROV) converted in an AUV. We propose several interesting contributions as a result of these tests, such as making the SAC control and guiding the AUV simultaneously, outperforming the PID controller in terms of energy saving, and reducing the amount of information needed by the SAC algorithm inputs. Moreover, our implementation of this controller allows facilitating the transfer towards real-world robots. The code corresponding to this work is available on GitHub

Sidescan sonar imagery segmentation with a combination of texture and spectral analysis

International audienceThis paper deals with the seabed classification from textured sonar images and specially the potential of the combination of features extracted from co-occurrences matrices and directional filter bank (DFB) . The texture analysis based on the co-occurrences matrices is strongly dependant on the choice of parameter values (e.g. the distance and the angular direction for the estimation of the number of transitions). In most cases the choice is not trivial. To get representative features from textures with different spatial frequencies, a comprehensive set of co-occurrence matrices with corresponding displacements and orientation has to be computed. In this work, we investigate a non classical approach based on the DFB. The approach uses a decomposition of the Fourier spectrum into three spectral bands: low, medium and high frequencies. A subsequent analysis of the pattern isotropy is conducted by dividing the medium spectral band into small, overlapped, angular sectors. The features extracted from this process are assessed so as to determine their potential on the classification performances. First, a comparison with classification performances result given by texture features derived from grey level co-occurrences matrices (GLCM) is made. Finally the global performance of the segmentation is assessed using the spectral features, the features extracted from GLCM and the grazing angle. The Klein 5000 experimental data used in this study have been acquired by DGA/GESMA during BP 02 experiment conducted by NURC

SPECTRAL DIRECTIONAL FILTER BANK FOR SIDESCAN SONAR SEGMENTATION WITH UNSUPERVISED NEURAL NETWORK APPROACH

International audienceThis paper deals with the unsupervised segmentation of textured seafloors from sidescan sonar imagery. The classical approaches for texture analysis are based on the estimation of co-occurrence matrices (which express the distribution of co-occurring values at a given offset) and the Haralick features derived from. However, the GLCM is strongly dependant on the parameterization of the offset (e.g. the distance d and the angular direction θ for the estimation of the number of co-occurring values). In most cases it is not at all obvious how such a choice should be made for (d, θ) and it is even more difficult to arrange for it to be made automatically. In this paper, we investigate the ability of another approach based on spectral features to discriminate between seabed textures. Spectral features are estimated from directional filter bank (DFB) in the 2DFourier space. A subsequent analysis of the pattern isotropy is conducted by dividing the medium spectral band into small, overlapped, angular sectors. The unsupervised segmentation used is a modified version of Kohonen SOFM (Self-Organizing Feature Maps) with splitting process of images to take into account the grazing angle dependency. The data used in our tests are sonar images recorded by Klein 5000 sidescan sonar

Analyse de descripteurs énergétiques et statistiques de signaux sonar pour la caractérisation des fonds marins

L'information contenue dans les images sonar, l'energie rétrodiffusée par l'interface sédimentaire, s'avère être un indice essentiel pour caractériser la nature et le relief du fond insonifié. les objectifs de cette thèse résident dans l'extraction et l'interprétation des données de deux systèmes sonar de me^me fréquence (100kHz) mais de géométrie différente. Dans un premier temps, les images sonar révèlent des artefacts. Nous proposons un algorithme de correction des données du sonar latéral en post-traitement, basé sur la reconstruction de la géométrie d'acquisition. L'étude a porté sur deux descripteur énergétiques et statistiques des signaux rétrodiffusés. L'indice angulaire de rétrodiffusion se révèle le descripteur le plus simple et le plus efficace pour la discrimination des fonds. La présence de textures dans les images autorise de compléter l'étude par un descripteur basé sur la forme des distributions statistiques. Différents comportements statistiques ont été mis en évidence en fonction du type de fond. Enfin, l'utilisation conjointe de ces deux descripteurs améliore les résultats de segmentation des images sonar; l'utilisation des "Support Vector machines" est proposée et s'est révélée pertinente et évolutive.Information contained in sonar data, the backscattered energy is well known as an essential clue about the seabed nature and roughness. The objectives are the data exploitation of two sonar systems, both operate at a high frequency (100 kHz) but their survey geometry is different. Sonar images reveal artefacts dur to geometry of the sonar system and array patterns. A new postprocessing correction method is proposed for signals recorded by the sidescan sonar, based on the reconstruction of the survey geometry. The study concerns two energetical and statistical features extracted from backscattered intensity. The angular backscattering strenght is shown as the simplest and the most efficient feature for the seabed discrimination. Textures presence in sonar images allow to complete the study by a feature based on the statistical distributions shape and revealing roughness characteristics. Different statistical behaviors are highlighted depending either on seafloor properties or on the sonar system geometry. Finally, the simultaneous use of these features improves segmentation results. In this context, the use of the "Support Vector Machines" is proposed and shows some relevant and evolutive possibilities.BREST-BU Droit-Sciences-Sports (290192103) / SudocPLOUZANE-Bibl.La Pérouse (290195209) / SudocSudocFranceF

SEAFLOOR CLASSIFICATION USING STATISTICAL MODELING OF WAVELET SUBBANDS

International audienceThis paper deals with the classification of textured seafloor images recorded bysidescan sonar. To address this problem, a supervised classification approach based onthe Bayesian framework is proposed. In this way, the textured images are characterizedthrough parametric probabilistic models of the wavelet coefficients. The generalizedGaussian distribution (GGD), which is a well-established model to characterize themarginal distributions of the wavelet subbands, is considered. However, to take intoaccount the joint statistics of wavelet coefficients, we also consider the Gaussian copulabased multivariate generalized Gaussian model (GC-MGG). A supervised learningcontext is adopted for the classification stage by using a probabilistic k-NearestNeighbors classifier. Each textured image will be represented by its GGD or GC-MGGestimated parameters and given a collection of training images the Kullback-Leiblerdivergence 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 thisapproac

DYNAMIC SELF-ORGANIZING ALGORITHM FOR UNSUPERVISED SEGMENTATION OF SIDESCAN SONAR IMAGES

International audienceThis paper deals with the dynamic neuronal approach for segmentation of textured seafloors from sidescan sonar imagery. For classical approaches of sonar images segmentation, the result of the classification is a set of sediment clusters representing the different kinds of seabed. However, those classical approaches give satisfying results only when a comprehensive training set is available. If the training set lacks a particular kind of seabed, it will be unknown for the classifier and the classification will be reduced to the closest known sediment cluster. As it is not always feasible to know the entire seabed types before the training phase, a dynamic algorithm solution capable of incremental learning has been developed. The Dynamic Self-organizing maps (DSOM) algorithm used in this work is an extension version of classical SOFM (Self-Organizing Feature Map) algorithm developed by Kohonen combined with Adaptive resonance Theory (ART). It is based on growing neuronal map size during the learning processes. Therefore, the size of the map is small in the beginning but increase dynamically using control vigilance threshold. To assess the consistency of the proposed approach, the DSOFM algorithm is tested on simulated data clusters and on real sonar data

Asymmetric power distribution model of wavelet subbands for texture classification

International audienceThe generalized Gaussian distribution (GGD) is a well established statistical model for wavelet subband characterization used in several applications. However, it is not really suitable for eventual asymmetry of probability density functions. Therefore, in this paper we propose to exploit the asymmetric power distribution (APD) which is a more general and flexible model than the GGD. The APD parameters are estimated through the maximum-likelihood estimation. A supervised texture classification problem is proposed as an application in this work. It is based on the Bayesian framework which has led to the definition of the closed form of the corresponding Kullback–Leibler divergence considered as a similarity measure. To validate the APD model, the goodness-of-fit using the classical Kolmogorov–Smirnov test is used. Finally, classification results on four databases demonstrate the interest of the proposed approach

Seafloor characterization for ATR applications using the monogenic signal and the intrinsic dimensionality

International audienceIn mine warfare context, environmental effects are known to degrade performances of most of automatic target recognition (ATR) processes. In this study, we consider the environment as an information that can be used to design a robust ATR process. Hence, we investigate a way to extract and exploit information about the seafloor using an isotropic analysis of sidescan sonar images based on the monogenic signal. This tool provides an orthogonal separation between energetic, geometrical and structural information of the 2D signal in a scale-space framework. It also allows to efficiently compute the continuous intrinsic dimensionality scale-space. We propose to use these last descriptors to characterize the sidescan sonar images in terms of homogeneous, anisotropic and complex areas. In each of these areas it can be expected that adapted ATR processes could be defined to outperform classical global approaches