Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation
This work addresses the challenge of limited ground reference data in forest monitoring for environmental scientists and remote sensing practitioners, offering an incremental improvement through semi-supervised learning.
The paper tackles the problem of predicting forest parameters from remote sensing data by imputing a small sample of ground reference data with pseudo-targets from existing prediction maps, which increases training data and enables deep learning for semi-supervised regression. The result shows that this approach surpasses traditional airborne laser scanning-based regression models in above-ground biomass and stem volume predictions across diverse forest types.
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.