Wavelet-based acoustic seabed discrimination system

Abstract

In almost any underwater operation that takes place on or under the seafloor, it is necessary to have an understanding of the makeup of the seabed below the silt and sediment. Sonar is an acoustic system extensively used for underwater inspection as well as seabed classification. In this paper, the problem of automatic segmentation and classification of seafloor using automatic acoustic seabed discrimination systems is discussed and a new split and merge algorithm based on the concept of standard wavelet transform is presented. Experimental results on true prototype data indicate the robustness and effectiveness of the proposed algorithm.

A review on the wavelet methods for sonar image segmentation

“…Figure 3 shows the experimental results of the above two methods. 64,65 It can be seen that there is almost no difference in the segmentation results between the two methods. However, the latter is fast enough to incorporate in a real-time seabed recognition (or segmentation) systems with low segmentation accuracy.…”

“…74 The GGD and the a-stable distribution Figure 3. Comparison of the experimental results: (a) original image in Javidan, 65 (b) segmentation result in Javidan and Eghbali, 64 (c) segmentation result in Javidan, 65 (a 0 ) original image in Javidan, 65 (b 0 ) segmentation result in Javidan and Eghbali, 64 and (c 0 ) segmentation result in Javidan. 65 parameters were used as the features of the sonar images.…”

“…The authors would like to thank Hong et al 40 , Chen et al 59 and Javidan et al 64,65 for their images being used in this review.…”

“…The method for characterizing the energy by means of the amplitude of the wavelet coefficients in the sub-bands had been proposed by Javidan. 64,65 Each highfrequency sub-band was partitioned into the nonoverlapping wavelet coefficient blocks with size M Â M. The energy value for each subblock was calculated by…”

“…On the basis of previous research, the standard wavelet transform was used, and the energy values of the detail sub-bands are used as features for classification in the same way. 65 Following distance evaluation, an algorithm for merging connected regions of two coarse segmented images was used to construct the final segmentation result. This method is also applicable in seabed recognition system with acceptable error rates.…”