Knowledge discovery in databases for sonar images classification
Sonar images classification is of great importance for various realistic applications such as submarine navigation or seabed mapping. Most approaches developed or used in the present work for seabed characterization are based on the use of texture analysis methods. Indeed, sonar images have different homogeneous areas of sediment that can be viewed as texture entities. Generally, texture features are numerous and not all are relevant; an extraction-reduction of these features seems necessary before the classification phase. We present in this work a complete chain for sonar images classification while optimizing the chain steps. We use the Knowledge Discovery in Databases (KDD) process for the chain development. The underwater environment is uncertain, which is reflected on the images obtained from the sensors used for their acquisition. Therefore, it is important to develop robust methods to these imperfections. We solve this problem in two different ways: a first solution is to make robust traditional classification methods, such as support vector machines or k-nearest neighbors, to these imperfections. A second solution is to model these imperfections to be taken into account by belief or fuzzy classification methods. We present the results obtained using different texture analysis approaches and classification approaches. We use other approaches based on the uncertain theories to overcome sonar images imperfections problem.
sonar images classification texture knowledge discovery on database svm belief svm fuzzy svm extraction -reduction evaluation