论文标题
一个新型的半经验的对比度回归框架，用于使用多传感器卫星数据
A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping with Multisensor Satellite Data
论文作者
论文摘要
森林的准确地图对于森林管理和碳库存监测至关重要。但是，深度学习在地球观察中变得越来越流行，但是，参考数据的可用性限制了其在广阔的森林映射中的潜力。为了克服这些局限性，在这里，我们将对比度回归引入基于EO的森林映射中，并开发出一种新型的半监视回归框架，用于连续森林变量的壁到墙映射。它结合了受监督的对比回归损失和半监督的跨十分回归损失。该框架在北方森林遗址上使用Copernicus Sentinel-1和Sentinel-2图像进行映射，以绘制森林树高度。与使用Vanilla UNET或传统回归模型相比，实现的预测准确性要好得多，其相对RMSE在架子水平上为15.1％。我们希望开发的框架可用于建模其他森林变量和EO数据集。
Accurate mapping of forests is critical for forest management and carbon stocks monitoring. Deep learning is becoming more popular in Earth Observation (EO), however, the availability of reference data limits its potential in wide-area forest mapping. To overcome those limitations, here we introduce contrastive regression into EO based forest mapping and develop a novel semisupervised regression framework for wall-to-wall mapping of continuous forest variables. It combines supervised contrastive regression loss and semi-supervised Cross-Pseudo Regression loss. The framework is demonstrated over a boreal forest site using Copernicus Sentinel-1 and Sentinel-2 imagery for mapping forest tree height. Achieved prediction accuracies are strongly better compared to using vanilla UNet or traditional regression models, with relative RMSE of 15.1% on stand level. We expect that developed framework can be used for modeling other forest variables and EO datasets.
