Autonomous Underwater Vehicles (AUV) equipped with advanced Aided Inertial Navigation Systems (AINS) and Synthetic Aperture Sonar (SAS) have over the last decade been increasingly used for detailed seabed surveying. This high-precision imagery has spurred development of repeat-pass processing methods, as the detection of seafloor changes over time is relevant for many applications, including mine hunting, infrastructure inspection and environmental monitoring. The main repeat-pass challenges are temporal decorrelation due to geophysical processes and biological activities, baseline decorrelation due to differences in sensing geometries and data co-registration requirements. We present techniques to mitigate these challenges, applied on HISAS imagery from HUGIN AUVs.

This presentation is part of Minisymposium “MS3 - Applications of Imaging Modalities beyond the Visible Spectrum (2 parts)”
organized by: Max Gunzburger (Florida State University) , G-Michael Tesfaye (Naval Surface Warfare Center, Panama City) , Janet Peterson (Florida State University) .

Authors:

Oivind Midtgaard (Norwegian Defence Research Establishment (FFI))

Torstein Saebo (Norwegian Defense Research Establishment (FFI))

Keywords:

change detection, computer vision, image registration, synthetic aperture sonar