Segmentation invariante en rasance des images sonar latéral par une approche neuronale compétitive

The sidescan sonar records the energy of an emitted acoustical wave backscattered by the seabed for a large range of grazing angles. The statistical analysis of the recorded signals points out a dependence according grazing angles, which penalizes the segmentation of the seabed into homogeneous regions. To improve this segmentation, classical approaches consist in compensating artifacts due to the sonar image formation (geometry of acquisition, gains, etc.) considering a flat seabed and using either Lambert’s law or an empirical law estimated from the sonar data. The approach chosen in this study proposes to split the sonar image into stripes in the swath direction; the stripe width being limited so that the statistical analysis of pixel values can be considered as independent of grazing angles. Two types of texture analysis are used for each stripe of the image. The first technique is based on the Grey-Level Co-occurrence Matrix (GLCM) and various Haralick attributes derived from. The second type of analysis is the estimation of spectral attributes. The starting stripe at mid sonar slant range is segmented with an unsupervised competitive neural network based on the adaptation of Self- Organizing Feature Maps (SOFM) algorithm. Then, from the knowledge acquired on the segmentation of this first stripe, the classifier adapts its segmentation to the neighboring stripes, allowing slight changes of statistics from one stripe to the other. The operation is repeated until the beginning and the end of the slant range are reached. The study made in this work is validated on real data acquired by the sidescan sonar Klein 5000. Segmentation performances of the proposed algorithm are compared with those of conventional approaches.Un sonar latéral de cartographie enregistre les signaux qui ont été rétrodiffusés par le fond marin sur une large fauchée. Les signaux sont ainsi révélateurs de l’interaction entre l’onde acoustique émise et le fond de la mer pour une large plage de variation de l’angle de rasance. L’analyse des statistiques de ces signaux rétrodiffusés montre une dépendance à ces angles de rasance, ce qui pénalise fortement la segmentation des images en régions homogènes. Pour améliorer cette segmentation, l’approche classique consiste à corriger les artefacts dus à la formation de l’image sonar (géométrie d’acquisition, gains variables, etc.) en considérant un fond marin plat et en estimant des lois physiques (Lambert, Jackson, etc.) ou des modèles empiriques. L’approche choisie dans ce travail propose de diviser l’image sonar en bandes dans le sens de la portée ; la largeur de ces bandes étant suffisamment faible afin que l’analyse statistique de la rétrodiffusion puisse être considérée indépendante de l’angle de rasance. Deux types d’analyse de texture sont utilisés sur chaque bande de l’image. La première technique est basée sur l’estimation d’une matrice des cooccurrences et de différents attributs d’Haralick. Le deuxième type d’analyse est l’estimation d’attributs spectraux. La bande centrale localisée à la moitié de la portée du sonar est segmentée en premier par un réseau de neurones compétitifs basé sur l’algorithme SOFM (Self-Organizing Feature Maps) de Kohonen. Ensuite, la segmentation est réalisée successivement sur les bandes adjacentes, jusqu’aux limites basse et haute de la portée sonar. A partir des connaissances acquises sur la segmentation de cette première bande, le classifieur adapte sa segmentation aux bandes voisines. Cette nouvelle méthode de segmentation est évaluée sur des données réelles acquises par le sonar latéral Klein 5000. Les performances de segmentation de l’algorithme proposé sont comparées avec celles obtenues par des techniques classiques

Range-independent segmentation of sidescan sonar images with competitive neural network

Un sonar latéral de cartographie enregistre les signaux qui ont été rétrodiffusés par le fond marin sur une large fauchée. Les signaux sont ainsi révélateurs de l’interaction entre l’onde acoustique émise et le fond de la mer pour une large plage de variation de l’angle de rasance. L’analyse des statistiques de ces signaux rétrodiffusés montre une dépendance à ces angles de rasance, ce qui pénalise fortement la segmentation des images en régions homogènes. Pour améliorer cette segmentation, l’approche classique consiste à corriger les artefacts dus à la formation de l’image sonar (géométrie d’acquisition, gains variables, etc.) en considérant un fond marin plat et en estimant des lois physiques (Lambert, Jackson, etc.) ou des modèles empiriques. L’approche choisie dans ce travail propose de diviser l’image sonar en bandes dans le sens de la portée ; la largeur de ces bandes étant suffisamment faible afin que l’analyse statistique de la rétrodiffusion puisse être considérée indépendante de l’angle de rasance. Deux types d’analyse de texture sont utilisés sur chaque bande de l’image. La première technique est basée sur l’estimation d’une matrice des cooccurrences et de différents attributs d’Haralick. Le deuxième type d’analyse est l’estimation d’attributs spectraux. La bande centrale localisée à la moitié de la portée du sonar est segmentée en premier par un réseau de neurones compétitifs basé sur l’algorithme SOFM (Self-Organizing Feature Maps) de Kohonen. Ensuite, la segmentation est réalisée successivement sur les bandes adjacentes, jusqu’aux limites basse et haute de la portée sonar. A partir des connaissances acquises sur la segmentation de cette première bande, le classifieur adapte sa segmentation aux bandes voisines. Cette nouvelle méthode de segmentation est évaluée sur des données réelles acquises par le sonar latéral Klein 5000. Les performances de segmentation de l’algorithme proposé sont comparées avec celles obtenues par des techniques classiques.The sidescan sonar records the energy of an emitted acoustical wave backscattered by the seabed for a large range of grazing angles. The statistical analysis of the recorded signals points out a dependence according grazing angles, which penalizes the segmentation of the seabed into homogeneous regions. To improve this segmentation, classical approaches consist in compensating artifacts due to the sonar image formation (geometry of acquisition, gains, etc.) considering a flat seabed and using either Lambert’s law or an empirical law estimated from the sonar data. The approach chosen in this study proposes to split the sonar image into stripes in the swath direction; the stripe width being limited so that the statistical analysis of pixel values can be considered as independent of grazing angles. Two types of texture analysis are used for each stripe of the image. The first technique is based on the Grey-Level Co-occurrence Matrix (GLCM) and various Haralick attributes derived from. The second type of analysis is the estimation of spectral attributes. The starting stripe at mid sonar slant range is segmented with an unsupervised competitive neural network based on the adaptation of Self- Organizing Feature Maps (SOFM) algorithm. Then, from the knowledge acquired on the segmentation of this first stripe, the classifier adapts its segmentation to the neighboring stripes, allowing slight changes of statistics from one stripe to the other. The operation is repeated until the beginning and the end of the slant range are reached. The study made in this work is validated on real data acquired by the sidescan sonar Klein 5000. Segmentation performances of the proposed algorithm are compared with those of conventional approaches

Unsupervised Knowledge Discovery of Seabed Types using Competitive Neural Network: Application to Sidescan Sonar Images

International audienceAbstract—The conventional approaches for habitats mapping based on supervised algorithms need a seabed ground truth classes to know the entire seabed types before the training phase. These 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 class. In addition, it is not always feasible to have a ground truth samples and generally costs are very important. This is what, automated sonar systems classification are becoming widely used. This paper is concerned with automated discovery of seabed types in sonar images. A novel unsupervised approach based on competitive artificial neural network (CANN) for sidescan sonar images segmentation is proposed. The main idea is to create an unsupervised color table which allows linking between the class color and the physical nature of the seabed. This process is based on these steps. The first one consists on texture features extraction from sonar images. Secondly, Self-Organizing features maps (SOFM) algorithm is used to project the vector features on two dimensional map. Then principal component analysis (PCA) is applied to reduce the dimensionality of the result of SOFM map to only three components. The three axes obtained by PCA process will be present the RGB color table. The final result of the color table can be used for supervised or unsupervised classification of sidescan sonar images

Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory

International audienceIn this paper we introduce a new unsupervised segmentation algorithm for textured sonar images. A Dynamic Self-Organizing Maps (DSOM) algorithm capable of incremental learning has been developed to automatically cluster the input data into relevant classes of seabed. DSOM algorithm is an extension of classical Self Organizing Maps (SOM) algorithm combined with Adaptive Resonance Theory (ART) technique. The proposed approach is based on growing map size during learning processes. Starting with a minimal number of neurons, the map size increases dynamically and the growth is controlled by the vigilance threshold of the ART network. To assess the consistency of the proposed approach, the DSOM algorithm is first tested on simulated data sets and then applied on real sidescan sonar images. The results obtained using the proposed approach demonstrate its capability to successfully cluster sonar images into their relevant seabed classes, very close to those resulting from human expert interpretation

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

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

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

Range-independent segmentation of sidescan sonar images with unsupervised SOFM Algorithm (Self-Organizing Feature Maps)

International audienceThe sidescan sonar records the energy of an emitted acoustical wave backscattered by the sea floor, orthogonally to the track followed. The statistical properties of the backscattered energy change with the nature of the sea floor, which allows for a segmentation of the seabed into homogeneous regions. However, the statistical description of the backscattering is not constant over the full swath of the sonar. Several parameters such as the geometry of the array or the time varying gain can be easily compensated or inverted. Making the backscattered energy independent of the grazing angle is a more difficult change, conventionally solved by considering a flat seabed and by using either Lambert's law or an empirical law estimated from the sonar data. To avoid the definition of a physical law describing the change in energy with grazing angle, the proposed algorithm divides the slant range into small stripes, where the statistics can be considered unaltered by the grazing angle variations. The starting stripe at mid sonar slant range is segmented with an unsupervised classifier based on the Kohonen algorithm SOFM (Self-Organizing Feature Maps). Then, from the knowledge acquired on the segmentation of this first stripe, the classifier adapts its segmentation to the neighboring stripes, allowing slight changes of statistics from one stripe to the other. The operation is repeated until the beginning and the end of the slant range are reached. Segmentation performances of the proposed algorithm are compared with those of conventional algorithms

Segmentation des images sonar latéral assurant l'invariance en rasance

WOSInternational audienceUn sonar latéral de cartographie enregistre les signaux qui ont été rétrodiffusés par le fond marin sur une large fauchée. Les signaux sont ainsi révélateurs de l'interaction entre l'onde acoustique émise et le fond de la mer pour une large plage de variation de l'angle de rasance. L'analyse des statistiques de ces signaux rétrodiffusés montre une dépendance à ces angles de rasance, ce qui pénalise fortement la segmentation des images en régions homogènes. Pour améliorer cette segmentation, l'approche classique consiste à corriger les artefacts dus à la formation de l'image sonar (géométrie d'acquisition, gains variables, etc.) en considérant un fond marin plat et en estimant des lois physiques (Lambert, Jackson, etc.) ou des modèles empiriques. L'approche choisie dans ce travail propose de diviser l'image sonar en bandes dans le sens de la portée; la largeur de ces bandes étant suffisamment faible afin que l'analyse statistique de la rétrodiffusion puisse être considérée indépendante de l'angle de rasance. Deux types d'analyse de texture sont utilisés sur chaque bande de l'image. La première technique est basée sur l'estimation d'une matrice des cooccurrences et de différents attributs d'Haralick. Le deuxième type d'analyse est l'estimation d'attributs spectraux. La bande centrale localisée à la moitié de la portée du sonar est segmentée en premier par un réseau de neurones compétitifs basé sur l'algorithme SOFM (Self-Organizing Feature Maps) de Kohonen. Ensuite, la segmentation est réalisée successivement sur les bandes adjacentes, jusqu'aux limites basse et haute de la portée sonar. A partir des connaissances acquises sur la segmentation de cette première bande, le classifieur adapte sa segmentation aux bandes voisines. Cette nouvelle méthode de segmentation est évaluée sur des données réelles acquises par le sonar latéral Klein 5000. Les performances de segmentation de l'algorithme proposé sont comparées avec celles obtenues par des techniques classiques comme l'algorithme K-moyennes (K-means)

Fatigue Evaluation and Scheduling for Manual Tasks: A Break Planning Approach

International audienceThe quality and time execution of any production task involving human operator depend on the state ofthe operator and its behaviour. Recently, several studies are interested in the modelling of human behaviour inindustrial system to design human centred manufacturing system. In this context, this paper focus of the fatigueof operator in production system and assume that the duration of any task will depend on his fatigue level. Itproposes a model of fatigue and task duration that include this level. We developed a simulator to show theeffect of the fatigue on the time duration of the task execution. We also propose a strategy to manage the levelof fatigue based on the definition of the number and duration of operator rest