Shallow Water Object Detection, Classification, and Localization via Phase-Measured, Bathymetry-Mode Backscatter
Abstract
Detection, classification, and localization (DCL) techniques are being developed around the use of a phase-measuring sidescan sonar (PMSS) in very shallow waters. The instrument simultaneously collects co-located sidescan imagery and bathymetry in extreme shallow water environments (<1 m water depth). In addition to the bathymetry, an uncalibrated backscatter data set, referred to in this study as phase-measured, bathymetry-mode backscatter (BMB), is also collected. This BMB has been minimally addressed in the literature. This work aims to use the BMB to detect and differentiate between various objects on the seafloor, including unexploded ordnance (UXO), and placed marine debris, or ‘clutter’, such as lobster pots, boat propellers, and car tires. The differentiation from multiple seafloor types including mud, sand, and gravel and different types of objects occurred through various statistical analysis methods including binomial and multinomial logistic regression. These methods have been applied to create statistical regression models for several variables including phase-measured, bathymetry-mode backscatter amplitude, sounding distance from nadir, per-ping vessel roll, orientation offset between per-ping vessel heading and object orientation, and all combinations of these variables. These statistical tests produced maximum likelihood odds ratios of individual soundings being associated with the various seafloor and object types. Results from these analyses shows that DCL could be possible with phase-measured, bathymetry-mode backscatter from this PMSS system, though these results may not be representative for all bed types and phase-measuring systems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 21, 2023
- Source ID
- 10.3390/rs15061685
Entities
People
- Bryan Mccormack
- Mark Borrelli
Organizations
- United States Department of Defense
- University of Massachusetts