Forest measurement is crucial for tracking climate change and quantifying the global carbon cycle. Synthetic Aperture Radar Tomography has become an effective technique to realize 3D forest structure monitoring. Recently a Deep Learning approach, named TomoSAR Neural Network (TSNN), has been proven a valid method for forest height and underlying topography estimation with polarimetric TomoSAR data. In this study, we evaluate the generalization ability of TSNN in working in different target areas, with different sensors and acquisition parameters. The experimental results demonstrate the robustness and generalization ability of TSNN.
Analysis of A Deep Learning Solution for Tomosar Forest Reconstruction
Yang, W.;Ferraioli, G.;Lu, X.;Pascazio, V.;Schirinzi, G.;Vitale, S.;
2024-01-01
Abstract
Forest measurement is crucial for tracking climate change and quantifying the global carbon cycle. Synthetic Aperture Radar Tomography has become an effective technique to realize 3D forest structure monitoring. Recently a Deep Learning approach, named TomoSAR Neural Network (TSNN), has been proven a valid method for forest height and underlying topography estimation with polarimetric TomoSAR data. In this study, we evaluate the generalization ability of TSNN in working in different target areas, with different sensors and acquisition parameters. The experimental results demonstrate the robustness and generalization ability of TSNN.File in questo prodotto:
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