TY - GEN
T1 - Exploiting phase information in synthetic aperture sonar images for target classification
AU - Williams, David P.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85059176797&partnerID=8YFLogxK
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U2 - 10.1109/OCEANSKOBE.2018.8559255
DO - 10.1109/OCEANSKOBE.2018.8559255
M3 - Conference contribution
AN - SCOPUS:85059176797
T3 - 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018
BT - 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018
Y2 - 28 May 2018 through 31 May 2018
ER -