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78 results on '"Marston, Timothy M."'

1. Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery

Author

Sledge, Isaac J., Byrne, Dominic M., King, Jonathan L., Ostertag, Steven H., Woods, Denton L., Prater, James L., Kennedy, Jermaine L., Marston, Timothy M., and Principe, Jose C.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a supervised manner, on image-level labels, to uncover a set of semi-sparse, spatially-discriminative regions in each image. The classification uncertainty of each region is then evaluated. Those areas with the lowest uncertainties are then chosen to be weakly labeled segmentation seeds, at the pixel level, for the second part of the framework. Each of the seed extents are progressively resized according to an unsupervised, information-theoretic loss with structured-prediction regularizers. This reshaping process uses multi-scale, adaptively-weighted features to delineate class-specific transitions in local image content. Content-addressable memories are inserted at various parts of our framework so that it can leverage features from previously seen images to improve segmentation performance for related images. We evaluate our weakly-supervised framework using real-world CSAS imagery that contains over ten seafloor classes and ten target classes. We show that our framework performs comparably to nine fully-supervised deep networks. Our framework also outperforms eleven of the best weakly-supervised deep networks. We achieve state-of-the-art performance when pre-training on natural imagery. The average absolute performance gap to the next-best weakly-supervised network is well over ten percent for both natural imagery and sonar imagery. This gap is found to be statistically significant., Comment: Submitted to the IEEE Journal of Oceanic Engineering

Published
2024

2. Motion tracking of fish and bubble clouds in synthetic aperture sonar data.

Author

Marston, Timothy M., Hall, Bernard R., Bassett, Christopher, Plotnick, Daniel S., and Kidwell, Autumn N.

Abstract

Data captured by a Synthetic Aperture Sonar (SAS) near Mobile Bay during the 2021 Undersea Remote Sensing experiment funded by the Office of Naval Research reveals near surface bubble clouds from wave breaking events and a large aggregation of fish. Tools developed for using SAS data to image hydrodynamic features in the water column were applied to observations of the bubble clouds and fish aggregation. Combining imagery and height data captured by the sonar array with a detection and tracking algorithm enables the trajectories, velocities, and behavior of fish in the aggregation to be observed. Fitting the velocity and height data of the tracked objects to a Gaussian mixture model and performing cluster analysis enables an estimate of the near-surface ambient velocity via observation of the movement of the bubble traces and the general direction of motion of the fish aggregation. We find that the velocity traces associated with bubbles are consistent with ambient currents as opposed to the direction of propagating wave crests while velocities of fish indicate relatively large, pelagic species. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Advances in synthetic aperture sonar transform mine countermeasures and undersea warfare

Author

Sternlicht, Daniel D., Fernandez, Jose E., and Marston, Timothy M.

Subjects
Submarine warfare -- Military aspects, Sonar systems -- Military aspects, Government, Political science
Abstract

ACTIVE SYNTHETIC APERTURE SONAR (SAS) IS A POWERFUL IMAGING TECHNIQUE THAT COHERENTLY COMBINES ECHOES FROM MULTIPLE PINGS ALONG THE TRAJECTORY OF A SURVEY PATH TO CONSTRUCT A LONG VIRTUAL ARRAY [...]

Published
2013

16. Automated Change Detection for Synthetic Aperture Sonar

Author

NAVAL SURFACE WARFARE CENTER PANAMA CITY DIV FL, G-Michael, Tesfaye, Marchand, Bradley, Tucker, J D, Sternlicht, Daniel D, Marston, Timothy M, Azimi-Sadjadi, Mahmood R, NAVAL SURFACE WARFARE CENTER PANAMA CITY DIV FL, G-Michael, Tesfaye, Marchand, Bradley, Tucker, J D, Sternlicht, Daniel D, Marston, Timothy M, and Azimi-Sadjadi, Mahmood R

Abstract

In this paper, an automated change detection technique is presented that compares new and historical seafloor images created with sidescan synthetic aperture sonar (SAS) for changes occurring over time. The method consists of a four stage process: a coarse navigational alignment; fine-scale co-registration using the scale invariant feature transform (SIFT) algorithm to match features between overlapping images; sub-pixel co-registration to improves phase coherence; and finally, change detection utilizing canonical correlation analysis (CCA). The method was tested using data collected with a high-frequency SAS in a sandy shallow-water environment. By using precise co-registration tools and change detection algorithms, it is shown that the coherent nature of the SAS data can be exploited and utilized in this environment over time scales ranging from hours through several days., Prepared in collaboration with the University of Washington Applied Physics Laboratory, Seattle, and the Colorado State University, Fort Collins.

Published
2014

22. Synthetic Aperture Sonar (SAS) and Acoustic Templates for the Detection and Classification of Underwater Munitions

Author

WASHINGTON UNIV SEATTLE APPLIED PHYSICS LAB, Kargl, Steven G, Williams, Kevin L, Espana, Aubrey L, Lim, Raymond, Kennedy, Jermaine L, Arrieta, Rudy T, Marston, Timothy M, Lopes, Joseph L, WASHINGTON UNIV SEATTLE APPLIED PHYSICS LAB, Kargl, Steven G, Williams, Kevin L, Espana, Aubrey L, Lim, Raymond, Kennedy, Jermaine L, Arrieta, Rudy T, Marston, Timothy M, and Lopes, Joseph L

Abstract

This presentation will present a series of monostatic and bistatic acoustic scattering measurements were conducted to investigate discrimination and classification capabilities based on the acoustic response of targets for underwater unexploded ordnance (UXO) applications. The measurements were performed during March 2010 and are referred to as the Pond Experiment 2010 (PondEx10), where the fresh water pond contained a sand sediment. The measurements utilized a rail system with a mobile tower and a stationary sonar tower. Each tower is instrumented with receivers while the sources are located on the mobile tower. For PondEx10, eleven targets were deployed at two distinct ground ranges from the mobile tower system. Acoustic data were initially processed using synthetic aperture sonar (SAS) techniques and the data were further processed to generate acoustic templates for the target strength as a function of frequency and aspect angle. Preliminary results of the processing of data collected from proud targets are presented. Also presented are the results associated with a processing technique that permits isolation of the response of an individual target, which is in close proximity to other targets. [Research supported by The Strategic Environmental Research and Development Program under projects MR-1665 and MR-1666 and the Office of Naval Research.], Presented at the 15th Annual Partners in Environmental Technology Technical Symposium & Workshop, 30 Nov - 2 Dec 2010, Washington, DC. Sponsored by SERDP and ESTCP.

Published
2010

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