Abstract
In 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.
Highlights
Image segmentation is an important step in the image analysis chain
Experimental tests are performed to show the capability of Dynamic Self-Organizing Maps (DSOM) algorithm for discovering incremental clusters
The second experiment demonstrates the application of the proposed DSOM algorithm on real sonar images
Summary
Image segmentation is an important step in the image analysis chain. It addresses the problem of dividing an images into homogeneous groups of pixels based on a similarity measure. As it is not always feasible to have seabed ground truth classes and to know the entire seabed types before the training phase, an unsupervised algorithm capable to determine clusters according to statistical similarity and independently to the expert interpretation is suitable for sonar images. The unsupervised approaches exploit the resemblance between statistics features estimated from images, with no a-priori knowledge about data labeling or number of classes. In this case, clustering algorithms are used to gather pixels or regions in similar groups. One of the important characteristic of SOM algorithm is its ability to preserve the topology of input space using neighborhood function It means that input data which is similar in term of features distance will be close after projection by SOM algorithm.
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