Patchwise Neural Network for Height Estimation in Forested Areas Based on Multichannel SAR Data

Tropical forests are a key component of the global carbon cycle and crucial in understanding the amounts of biomass. With plans for upcoming space-borne missions like BIOMASS to monitor forestry, several airborne missions, including TropiSAR and AfriSAR campaigns, have been successfully launched and experimented. Thanks to these campaigns and the availability of SAR and LiDAR date, recently deep learning solution for the forest height estimation have been proposed. Indeed, interesting results have been achieved and have proven the effectiveness of deep learning in accurately estimating forest and ground heights by utilizing the elements of the covariance matrix derived from multichannel SAR data on a pixel-by-pixel basis. The aim of this work is to go beyond the pixel-wise approach and introducing a patch-wise deep learning solution. The idea is to utilize a three-dimensional array composed of elements from the covariance matrix of a patch of multichannel SAR data as its input data. The solution is able to leverage patch-based information and extract spatial features from neighboring pixels’ heights to improve estimation accuracy. In experiments, the training is conducted by considering SAR data as the input and Light Detection and Ranging (LiDAR) values as the ground truth, and results show striking advantages in both performance and generalization ability by leveraging on spatial information.