Challenges of Automated Change Detection in Repeat-pass Synthetic Aperture Sonar Imagery

Abstract

Change detection extracts regions of interest as temporal differences by imaging a scene before and after changes may have occurred. We denote the initial image as the reference and the more recent as the current image. Automated tools for image comparison are a prerequisite for many change detection applications, because the large data rates and detailed, yet repetitive contents of the imagery make full manual analysis tedious and prone to errors. Although automated change detection (ACD) in repeat-pass imagery is a well-established technique for synthetic aperture radar (SAR) 1, it is far less mature for underwater sonar due to inherent properties of both traditional side-scan sonar (SSS) imaging and the complex ocean environment. These properties impede for example sensor trajectory control, data positioning accuracy and image resolution. The increasing use of autonomous underwater vehicles (AUVs) equipped with advanced synthetic aperture sonar (SAS) and aided inertial navigation systems (AINS) has partly remedied these limitations and facilitated the development of ACD methods for the sonar domain 2-5. Still, important challenges remain, in particular regarding scene stability, sensing consistency and image alignment between surveys.