DL Based Forest Height Reconstruction Using Single-Pol Tomosar Images

Forests play an important role in the global carbon cycle, and subsequently global climate change. Synthetic Aperture Radar Tomography (TomoSAR) can achieve three-dimensional forest structures relying on the multibaseline image acquisition. At present, plenty of TomoSAR approaches are based on fully polarimetric TomoSAR datasets which require costly data acquisition. The aim of this paper is to exploit the potential of deep learning for retrieving forest height by using single polarimetric data, going beyond the limitation of the requirement for full polarization. We design a fully connected network handling the forest height reconstruction problem from a classification task perspective. The network is trained using the covariance matrix elements of single polarimetric images acquired by ONERA over Paracou region as input, while LiDAR data acts as reference. Experimental results generally show good performance for forest height and underlying topography reconstruction and, a good robustness if compared with the results driven by fully polarimetric images.