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A Deep Learning Solution for Height Inversion on Forested Areas Using Single and Dual Polarimetric TomoSAR.
- Source :
- IEEE Geoscience & Remote Sensing Letters; 2023, Vol. 20, p1-5, 5p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1545598X
- Volume :
- 20
- Database :
- Complementary Index
- Journal :
- IEEE Geoscience & Remote Sensing Letters
- Publication Type :
- Academic Journal
- Accession number :
- 176253657
- Full Text :
- https://doi.org/10.1109/LGRS.2023.3322782