Bridging Depths: Dynamic Geometric Proxies and Neural Networks for Robust Oceanic Feature Representation

Farheen Fauziya; Ananya Sen Gupta; Andrew Christensen; Aniali Mathews; Subhaiit Das; Ivars Kirsteins

doi:10.1109/OCEANS55160.2024.10753860

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Bridging Depths: Dynamic Geometric Proxies and Neural Networks for Robust Oceanic Feature Representation

Conference proceeding

Peer reviewed

Bridging Depths: Dynamic Geometric Proxies and Neural Networks for Robust Oceanic Feature Representation

Farheen Fauziya, Ananya Sen Gupta, Andrew Christensen, Aniali Mathews, Subhaiit Das and Ivars Kirsteins

Oceans (New York. Online), pp.1-4

09/23/2024

DOI: 10.1109/OCEANS55160.2024.10753860

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Abstract

Robust feature representation poses significant challenges in autonomous sonar target recognition, exacerbated by unpredictable acoustic scatter, underwater multipath effects, and uncertainty in target geometry. Although machine learning techniques have been widely applied, there remains a gap in the numerical and theoretical studies addressing these complex morphing geometries. Specifically, the impact of nonlinear overlapping and dynamically varying parameters on surrounding geometries remains unexplored. This position paper explores the potential of dynamic geometric proxies, human features, and other natural patterns to tackle the challenges of robust feature representation in autonomous sonar target recognition. Using proxies such as braids, knots, and links, we aim is to establish reliable ground truths to interpret sonar features. Our methodology involves extending to state-of -the-art (SOTA) architectures such as Vision Transformers (ViTs) utilizing standard CNN backbones such as ResNet to extract distinctive features from filtered ping responses and geometrically represent them. Additionally, we explore the augmentation of datasets by generating geometric proxies from human features that exhibit similar geometric patterns. Recent research indicates that incorporating dynamic human features as geometric proxies for acoustic representation in the ocean can broaden sonar applications to include signal processing for autonomous target recognition. To realize this potential, this study recommends the creation and collection of target category signatures, as well as their geometric structures. The given signatures will be converted into powerful class/category features using the trained Vision Transformer model. Our approach proposes a novel perspective on feature extraction for ocean-related applications, aiming to improve underwater signal processing and acoustic analysis. By developing geometric proxies and training neural networks based on sound ground truths, this research is expected to significantly advance sonar target recognition capabilities significantly.

Computer Vision

Geometry

Signal Processing

Acoustic applications

Acoustic signal processing

Autonomous target detection

Braids

Convolutional Neural Networks (CNNs)

Dataset augmentation

Dynamic geometric proxies

Feature extraction

Feature Representation

Geometric

Knots and Links

Neural networks

Ocean acoustic applications

Oceans

Patterns in Sonar Data

Sonar target recognition

Target recognition

Transformers

Uncertainty

Underwater signal processing

Vision Transformers (ViTs)

Details

Title: Subtitle: Bridging Depths: Dynamic Geometric Proxies and Neural Networks for Robust Oceanic Feature Representation
Creators: Farheen Fauziya - University of Iowa
Ananya Sen Gupta - University of Iowa
Andrew Christensen - University of Iowa
Aniali Mathews
Subhaiit Das
Ivars Kirsteins - Naval Undersea Warfare Center
Resource Type: Conference proceeding
Publication Details: Oceans (New York. Online), pp.1-4
Publisher: IEEE
DOI: 10.1109/OCEANS55160.2024.10753860
eISSN: 2996-1882
Language: English
Date published: 09/23/2024
Academic Unit: Electrical and Computer Engineering; Iowa Technology Institute
Record Identifier: 9984757068802771

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