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A method for generating forest/non-forest maps from TanDEM-X interferometric data.

, , , and . IGARSS, page 2634-2637. IEEE, (2015)

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TANDEM-X height performance and data coverage., , and . IGARSS, page 4088-4091. IEEE, (2017)Radar Backscatter Mapping Using TerraSAR-X., , , and . IEEE Trans. Geosci. Remote. Sens., 49 (10): 3538-3547 (2011)Comparative Analysis of the Global Forest/Non-Forest Maps Derived from SAR and Optical Sensors. Case Studies from Brazilian Amazon and Cerrado Biomes., , , , , , , and . Remote. Sens., 13 (3): 367 (2021)A Fully Automatic Algorithm for Editing the TanDEM-X Global DEM., , , , and . Remote. Sens., 12 (23): 3961 (2020)Φ-Net: Deep Residual Learning for InSAR Parameters Estimation., , , and . IEEE Trans. Geosci. Remote. Sens., 59 (5): 3917-3941 (2021)Global Interferometric Coherence Maps From TanDEM-X Quicklook Data., , and . IEEE Geosci. Remote. Sens. Lett., 11 (11): 1861-1865 (2014)TanDEM-X Forest Mapping Using Convolutional Neural Networks., , , and . Remote. Sens., 11 (24): 2980 (2019)On the Derivation of Volume Decorrelation From TanDEM-X Bistatic Coherence., , , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2022)Potential of Deep Learning for Forest Height Estimation from Tandem-X Bistatic Insar Data., , , and . IGARSS, page 1481-1484. IEEE, (2023)Production of a global forest/non-forest map utilizing TanDEM-X interferometric SAR data., , , , , and . IGARSS, page 751-754. IEEE, (2017)