Some improvements of a rotation invariant autoregressive method: Application to the neural classification of noisy sonar images

This paper presents some improvements of a rotation invariant method based on AutoRegressive (AR)2DModelstoclassify textures. The basic model and our improved version are applied to natural sidescan sonar images (with multiplicative noise) in order to extract a reduced set of relevant rotation invariant features which are then used to feed a MultiLayer Perceptron (MLP) for identi cation task. Some analysis are conducted over these features to evaluate their interest. Classi cation results on four types of sidescan sonar images illustrate the e ciency of the proposed approach