Deep Learning for Forest Canopy Height Estimation from SAR

Accurate estimation of forest height plays a vital role in forest cover mapping and monitoring logging activities. In this work, a deep learning-based methodology is explored to measure forest canopy height from InSAR products and Single Look Complex SAR images (SLCs) to fully capture the complex information available in radar data and minimize the error in forest height estimates caused by various processing steps. The proposed deep-learning model consists of an U-net architecture that is used to regress the forest height from different input features. LiDAR LVIS measurements serve as reference data. This framework is used to assess the impact of different input features that are at different processing levels and their effect on model performance. We use TanDEM-X SAR images and LiDAR data from the AfriSAR campaign over Gabon, Africa. The results show the potential of the proposed approach in achieving more accurate forest height estimates if data is represented as complex coherence instead of as volume decorrelation or SLCs.