Sparse Representation based Classification for mine hunting using Synthetic Aperture Sonar

In this paper, a Sparse Representation based Classification (SRC) approach is employed for mine hunting using Synthetic Aperture Sonar (SAS) images. Given a training database with enough samples, SRC exploits the properties of sparse signals and expresses a sample of unknown class as a sparse linear combination of the training samples. The class of the training samples with greater weight is likely to be the candidate sample class. The method was introduced for face recognition, where the face images are directly taken as feature sets. Due to the greater variability of sonar images, for mine hunting applications it is more convenient to transform the image samples into a different feature domain. Several feature sets are considered, and the results are compared with those provided by a linear discriminant analysis classifier. We have tested the method on an extensive SAS database with more than 400 mines.