基于伪目标插补训练的多目标深度学习回归模型的森林参数预测
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation
AI总结：
本文提出利用遥感预测图作为伪目标进行半监督插补，结合多目标深度学习回归模型，在坦桑尼亚和挪威的森林参数预测中超越了传统ALS方法。
摘要：
在利用遥感（RS）数据预测森林参数时，传统上回归模型是在少量地面参考数据样本上训练的。本文提出用现有的基于遥感的预测图中的数据来插补真实预测目标样本，我们将这些数据视为伪目标。这大幅增加了目标训练数据的数量，并利用深度学习（DL）进行半监督回归建模。我们使用由机载激光扫描（ALS）数据构建的预测图来提供准确的伪目标，并将哨兵一号（Sentinel-1）的C波段合成孔径雷达（SAR）免费数据作为回归变量。我们采用改进的U-Net架构，并选择不同的训练目标。我们证明，当使用合理的损失函数组合时，半监督插补策略产生的结果优于传统的基于ALS的回归模型，尽管哨兵数据被认为在森林监测方面较为逊色。这些结果在坦桑尼亚的地上生物量预测和挪威的树干材积预测实验中保持一致，代表了参数和森林类型的多样性，强调了该方法的稳健性。
英文摘要：
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.
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