Deep recurrent neural networks for land-cover classification using sentinel-1 insar time series

Shaojia Ge, Oleg Antropov, Weimin Su, Hong Gu, Jaan Praks

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

9 Citations (Scopus)

Abstract

To date, the potential of multitemporal interferometric SAR (InSAR) data in land-cover mapping has not been fully explored despite suitable time series increasingly acquired from SAR sensors. Here, we suggest to use an LSTM (Long Short Term Memory) based land-cover classifier to address this problem. Spatial context is preserved by using grey-level spatial dependencies and morphological profiles. Further, a 4-LSTM-based model was trained to capture the temporal dynamics of InSAR coherence. Altogether 39 Sentinel-1 interferometric coherence pairs acquired over Donana in Spain were used to evaluate the method performance. Achieved more than 90% overall accuracy indicates the strong potential of developed InSAR recurrent approach in improving differentiation between various land cover classes.

Original languageEnglish
Title of host publicationIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages473-476
Number of pages4
ISBN (Electronic)978-1-5386-9154-0, 978-1-5386-9153-3
ISBN (Print)978-1-5386-9155-7
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019
https://igarss2019.org

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Abbreviated titleIGARSS 2019
Country/TerritoryJapan
CityYokohama
Period28/07/192/08/19
Internet address

Funding

This study was supported by the National Natural Science Foundation of China (Grant Nos. 61671246). InSAR coherence time series were provided in the framework of the ESA SEOM SInCohMap project.

Keywords

  • Coherence
  • GLCM
  • Land-cover mapping
  • LSTM
  • Morphological profile
  • Recurrent neural networks
  • Sentinel- 1
  • Synthetic aperture radar (SAR)
  • Time series

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