Forest Parameter Prediction by Multiobjective Deep Learning of
  Regression Models Trained with Pseudo-Target Imputation
Sara Björk Stian N. Anfinsen Michael Kampffmeyer Erik Næsset Terje Gobakken Lennart Noordermeer
In prediction of forest parameters with data from remote sensing (RS),
regression models have traditionally been trained on a small sample of ground
reference data. This paper proposes to impute this sample of true prediction
targets with data from an existing RS-based prediction map that we consider as
pseudo-targets. This substantially increases the amount of target training data
and leverages the use of deep learning (DL) for semi-supervised regression
modelling. We use prediction maps constructed from airborne laser scanning
(ALS) data to provide accurate pseudo-targets and free data from Sentinel-1's
C-band synthetic aperture radar (SAR) as regressors. A modified U-Net
architecture is adapted with a selection of different training objectives. We
demonstrate that when a judicious combination of loss functions is used, the
semi-supervised imputation strategy produces results that surpass traditional
ALS-based regression models, even though \sen data are considered as inferior
for forest monitoring. These results are consistent for experiments on
above-ground biomass prediction in Tanzania and stem volume prediction in
Norway, representing a diversity in parameters and forest types that emphasises
the robustness of the approach.