A Framework for Acoustic Segmentation Using Order Statistic-Constant False Alarm Rate in Two Dimensions From Sidescan Sonar Data

This paper describes a framework for object segmentation from sidescan sonar acoustic data. The current techniques consume a great deal of computational resources to accurately carry out object segmentation. They also involve the tuning of many parameters to obtain good quality images. This is due to the handling of the large data volume generated by these devices and environmental fluctuations such as salinity, density, temperature, and others variations. The framework proposed uses a migration and adaptation of a technique widely used in radar technology for detecting moving objects. This radar technique is known as order statistic-constant false alarm rate (OS-CFAR) applied in 2-D. OS-CFAR 2-D rank orders the samples obtained from a sliding window to make a segmentation of the image. This segmentation is done into several types of regions: acoustic highlight, shadow, and different seafloor reverberation areas. OS-CFAR 2-D is less sensitive than other methods to the presence of the speckle noise due to the use of order statistics. This proposal was contrasted experimentally on real images. Likewise, an experimental comparison with the results obtained with the undecimated discrete wavelet transform, active contours, Markov random field, and accumulated cell averaging CFAR applied in two dimensions technique is also presented.