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From Patches to Deep Learning: Combining Self-Similarity and Neural Networks for Sar Image Despeckling

  • University of Lyon
  • Centre national de la recherche scientifique
  • Université Paris-Saclay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Citations (Scopus)

Abstract

Speckle reduction has benefited from the recent progress in image processing, in particular patch-based non-local filtering and deep learning techniques. These two families of methods offer complementary characteristics but have not yet been combined. We explore strategies to make the most of each approach.

Original languageEnglish
Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5113-5116
Number of pages4
ISBN (Electronic)9781538691540
DOIs
Publication statusPublished - 1 Jul 2019
Externally publishedYes
Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 2019 → 2 Aug 2019

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
ISSN (Print)2153-6996
ISSN (Electronic)2153-7003

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/TerritoryJapan
CityYokohama
Period28/07/19 → 2/08/19

Keywords

  • PolSAR
  • SAR
  • deep learning
  • non-local filtering
  • patches
  • speckle

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