A Deep Learning Solution for Height Inversion on Forested Areas Using Single and Dual Polarimetric TomoSAR

Wenyu Yang, Sergio Vitale, Hossein Aghababaei, Giampaolo Ferraioli, Vito Pascazio*, Gilda Schirinzi

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
11 Downloads (Pure)

Abstract

Forest characterization and monitoring are highly important for tracking climate change, using ecology resources, and biodiversity applications. Synthetic aperture radar tomography (TomoSAR) provides the opportunity to reconstruct 3-D structures of the penetrable media relying on multibaseline image acquisition. In forest applications, TomoSAR serves as a powerful technical tool for reconstructing forest height and underlying topography. Presently, a number of reconstruction methods are based on fully polarimetric (FP) TomoSAR (Pol-TomoSAR) datasets which require costly data acquisition. The aim of this letter is to go beyond the limitation of the requirement for full polarization by extending tomographic SAR neural network (TSNN), a neural network for TomoSAR, to the case of single-polarimetric (SP) and dual-polarimetric (DP) TomoSAR data for retrieving forest height and underlying topography. Experimental results indicate that TSNN trained by SP or DP TomoSAR data is a powerful candidate to estimate forest height and underlying topography with high accuracy.

Original languageEnglish
Article number2505605
Pages (from-to)1-5
Number of pages5
JournalIEEE geoscience and remote sensing letters
Volume20
DOIs
Publication statusPublished - 16 Oct 2023

Keywords

  • Deep learning (DL)
  • forest height
  • polarimetry
  • synthetic aperture radar (SAR)
  • tomography
  • underlying topography
  • 2024 OA procedure
  • ITC-ISI-JOURNAL-ARTICLE

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