OS-CFAR process in 2-D for object segmentation from Sidescan Sonar data

This paper describes a technique to object segmentation from Side Scan Sonar (SSS) acoustical data. The current techniques consume a great deal of computational resources to accurately carry out objects 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 with salinity, density, temperature and others variations. The technique proposed in this work consists of 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 two dimensions. OS-CFAR 2D rank orders the samples obtained from a sliding window to make a segmentation of the image. This segmentation is done into three types of regions: acoustical highlight, shadow, and seafloor reverberation areas. OS-CFAR 2D 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 (UDWT), Active Contours and ACA-CFAR 2D (Accumulated Cell Averaging — CFAR in 2D) technique is also presented.