A Deep Learning Framework for the Estimation of Forest Height From Bistatic TanDEM-X Data
Author:
Affiliation:
1. Microwaves and Radar Institute, German Aerospace Center, Weßling, Germany
2. INRAE, Montpellier, France
3. Department of Information Engineering and Computer Science, University of Trento, Povo, Italy
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9973430/10234548.pdf?arnumber=10234548
Reference71 articles.
1. Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms
2. The global forest/non-forest map from TanDEM-X interferometric SAR data
3. Using Landsat-8 and Sentinel-1 data for Above Ground Biomass assessment in the Tamar valley and Dartmoor
4. Importance of bistatic SAR features from TanDEM-X for forest mapping and monitoring
5. The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions
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1. Generation of country-scale canopy height maps over Gabon using deep learning and TanDEM-X InSAR data;Remote Sensing of Environment;2024-09
2. Country-Scale Mapping of Forest Parameters Using Deep Learning and Tandem-X Insar Data;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
3. Characterization of the Amazon Rainforest Backscatter at X-Band Using TanDEM-X Data;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
4. A Novel Phase Unwrapping Method for Low Coherence Interferograms in Coal Mining Areas Based on a Fully Convolutional Neural Network;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
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