标题： 一种用于多传感器卫星数据森林清查制图的新型半监督对比回归框架 显示英文标题

标题： A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping with Multisensor Satellite Data

摘要： 准确绘制森林地图对于森林管理和碳储量监测至关重要。深度学习在地球观测 (EO) 中变得越来越流行，然而，参考数据的可用性限制了其在大面积森林制图中的潜力。为克服这些局限性，我们在此将对比回归引入基于 EO 的森林制图，并开发了一种新的半监督回归框架，用于连续森林变量的全域制图。该框架结合了有监督对比回归损失和半监督交叉伪回归损失。该框架在一个北方森林站点上通过使用哨兵-1 和哨兵-2 影像进行森林树高制图时得到了验证。与使用普通 UNet 或传统回归模型相比，预测精度显著提高，在林分水平上的相对均方根误差为 15.1%。我们预计，所开发的框架可以用于建模其他森林变量和 EO 数据集。 显示英文摘要

摘要： 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.