Exploiting Phase Information in Synthetic Aperture Sonar Images for Target Classification

Abstract

It is demonstrated that the phase information present in complex high-frequency synthetic aperture sonar(SAS) imagery can be exploited for successful object classification. That is, without using the amplitude content of the imagery, man-made targets can be discriminated from naturally occurring clutter. To exploit the information ostensibly hidden in the phase imagery, relatively simple convolutional neural networks (CNNs)are trained, from scratch, on a large database of SAS phase images collected at sea. Inference is then performed on real SAS data collected at sea during five other surveys that span multiple geographical locations and a variety of seafloor types and conditions. These experimental results on the test data illustrate that the phase information alone can produce favorable object classification performance. To our knowledge, this work is the first to demonstrate this finding.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2019
Accession Number
AD1113326

Entities

People

  • David P. Williams

Organizations

  • Centre for Maritime Research and Experimentation

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Amplitude
  • Artificial Intelligence Software
  • Autonomous Underwater Vehicles
  • Classification
  • Computer Vision
  • Convolutional Neural Networks
  • Data Sets
  • Detection
  • Filters
  • Image Segmentation
  • Images
  • Neural Networks
  • Target Classification
  • Test Sets
  • Three Dimensional
  • Training
  • Underwater Vehicles

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks