Raytheon has extensively processed high-resolution sonar images with its CAD/CAC algorithms to provide real-time classification of mine-like bottom objects in a wide range of shallow-water environments. The algorithm performance is measured in terms of probability of correct classification (Pcc) as a function of false alarm rate, and is impacted by variables associated with both the physics of the problem and the signal processing design choices. Some examples of prominent variables pertaining to the choices of signal processing parameters are image resolution (i.e., pixel dimensions), image normalization scheme, and pixel intensity quantization level (i.e., number of bits used to represent the intensity of each image pixel). Improvements in image resolution associated with the technology transition from sidescan to synthetic aperture sonars have prompted the use of image decimation algorithms to reduce the number of pixels per image that are processed by the CAD/CAC algorithms, in order to meet real-time processor throughput requirements. Additional improvements in digital signal processing hardware have also facilitated the use of an increased quantization level in converting the image data from analog to digital format. This study evaluates modifications to the normalization algorithm and image pixel quantization level within the image processing prior to CAD/CAC processing, and examines their impact on the resulting CAD/CAC algorithm performance. The study utilizes a set of at-sea data from multiple test exercises in varying shallow water environments.

Date Published: 7 May 2007
PDF: 9 pages
Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 65530T (7 May 2007); doi: 10.1117/12.718764

Published in SPIE Proceedings Vol. 6553:
Detection and Remediation Technologies for Mines and Minelike Targets XII
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)