Missing Data Recovery via Deep Networks for Limited Ground Penetrating Radar Measurements


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Kumlu D., Tas K., Erer I.

REMOTE SENSING, vol.14, no.3, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.3390/rs14030754
  • Journal Name: REMOTE SENSING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, Compendex, INSPEC, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: ground-penetrating radar, matrix completion, deep image prior, deep learning, data recovery, SEISMIC DATA RECONSTRUCTION, MATRIX COMPLETION, LOW-RANK, ALGORITHM, REMOVAL
  • Istanbul Technical University Affiliated: Yes

Abstract

Missing data problem frequently occurs during data acquisition in ground-penetrating radar (GPR) and recovery of the missing entries prior to any processing is vital in GPR imaging. Existing missing data recovery methods are based on low-rank matrix completion or the recently proposed deep generative networks. However, the former approaches suffer from producing satisfying results under severe missing data cases and the latter require a large amount of data for training. This study proposes two methods based on deep networks for the missing data recovery. The first method uses pyramid-context encoder network (PEN-Net) architecture which consists of three parts: attention transfer network, guided Pyramid-context encoder, and a multi-scale decoder. Although the method needs training, it requires considerably less data compared to the existing U-Net based method. The second method, deep image prior (DIP), is a regularization based data recovery method which uses an untrained network as a prior. This method does not need any training, network weights are initialized randomly and updated during the iterations to minimize the cost function. Different experiments are reported for both pixel and column-wise missing cases in simulated and real data. The simulated data results show that the proposed methods have a noticeably better performance than conventional methods for the challenging pixel-wise case around 17-27% and moderate level column-wise missing case around 15%. Besides, they can also deal with extreme column-wise missing data cases where the conventional methods fail completely. Real data results further verify the superiority of the proposed methods.