OBJECT CLASSIFICATION IN SIDESCAN SONAR IMAGES WITH SPARSE REPRESENTATION TECHNIQUES

Most supervised classification approaches try to learn patterns in inter class variabilities using training samples. However in the real world, their discriminative power is often diminished, because data is seldom free from irregularities within a class. Apriori modeling of these intra class variabilities poses a challenge even in underwater sidescan sonar images that we consider for object classification in this work. Sparse representation techniques prove particularly useful in this regard because of their data driven approach to model these variabilities. Results on the NSWC sidescan sonar database suggest that sparse representation classifier with zernike magnitude features is significantly robust in the presence of these non-idealities.