%0 Conference Proceedings
%T Range-independent segmentation of sidescan sonar images with unsupervised SOFM algorithm (self-organizing feature maps).
%+ Lab-STICC_ENSTAB_CID_SFIIS ; OSM
%A Nait-Chabane, Ahmed
%A Zerr, Benoit
%A Le Chenadec, Gilles
%< avec comitÃ© de lecture
%( ECUA 2012
%B ECUA 2012
%C Edimburgh, United Kingdom
%P xx
%8 2012-07-02
%D 2012
%K Seafloor Segmentation
%K Texture Analysis
%K Unsupervised classification
%K SOFM
%Z Engineering Sciences [physics]/Signal and Image processing
%Z Computer Science [cs]/Signal and Image ProcessingConference papers
%X The 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.
%G English
%2 https://ensta-bretagne.hal.science/hal-00728978/document
%2 https://ensta-bretagne.hal.science/hal-00728978/file/A.Nait-Chabanne_ECUA12.pdf
%L hal-00728978
%U https://ensta-bretagne.hal.science/hal-00728978
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