Deep network for simultaneous decomposition and classification in UWB-SAR imagery

Tiep H. Vu, Lam Nguyen, Tiantong Guo, Vishal Monga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Classifying buried and obscured targets of interest from other natural and manmade clutter objects in the scene is an important problem for the U.S. Army. Targets of interest are often represented by signals captured using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology. This technology has been used in various applications, including ground penetration and sensing-through-the-wall. However, the technology still faces a significant issue regarding low-resolution SAR imagery in this particular frequency band, low radar cross sections (RCS), small objects compared to radar signal wavelengths, and heavy interference. The classification problem has been firstly, and partially, addressed by sparse representation-based classification (SRC) method which can extract noise from signals and exploit the cross-channel information. Despite providing potential results, SRC-related methods have drawbacks in representing nonlinear relations and dealing with larger training sets. In this paper, we propose a Simultaneous Decomposition and Classification Network (SDCN) to alleviate noise inferences and enhance classification accuracy. The network contains two jointly trained sub-networks: the decomposition sub-network handles denoising, while the classification sub-network discriminates targets from confusers. Experimental results show significant improvements over a network without decomposition and SRC-related methods.

Original languageEnglish (US)
Title of host publication2018 IEEE Radar Conference, RadarConf 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages553-558
Number of pages6
ISBN (Electronic)9781538641675
DOIs
StatePublished - Jun 8 2018
Event2018 IEEE Radar Conference, RadarConf 2018 - Oklahoma City, United States
Duration: Apr 23 2018Apr 27 2018

Publication series

Name2018 IEEE Radar Conference, RadarConf 2018

Other

Other2018 IEEE Radar Conference, RadarConf 2018
CountryUnited States
CityOklahoma City
Period4/23/184/27/18

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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    Vu, T. H., Nguyen, L., Guo, T., & Monga, V. (2018). Deep network for simultaneous decomposition and classification in UWB-SAR imagery. In 2018 IEEE Radar Conference, RadarConf 2018 (pp. 553-558). (2018 IEEE Radar Conference, RadarConf 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RADAR.2018.8378619