Recovery of impenetrable rough surface profiles via CNN-based deep learning architecture

Aydin I., Budak G., Sefer A., Yapar A.

INTERNATIONAL JOURNAL OF REMOTE SENSING, vol.43, no.15-16, pp.5658-5685, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 15-16
  • Publication Date: 2022
  • Doi Number: 10.1080/01431161.2022.2105177
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.5658-5685
  • Keywords: Convolutional neural network, deep learning, inverse scattering problems, rough surface imaging, RECONSTRUCTION
  • Istanbul Technical University Affiliated: Yes


In this paper, a convolutional neural network (CNN)-based deep learning (DL) architecture for the solution of an electromagnetic inverse problem related to imaging of the shape of the perfectly electric conducting (PEC) rough surfaces is addressed. The rough surface is illuminated by a plane wave and scattered field data is obtained synthetically through the numerical solution of surface integral equations. An effective CNN-DL architecture is implemented through the modelling of the rough surface variation in terms of convenient spline type base functions. The algorithm is numerically tested with various scenarios including amplitude only data and shown that it is very effective and useful.