用深度学习精准识别圣诞树种植园，解决混淆与稀有目标难题。
Detection of Christmas tree plantations from high-resolution aerial imagery. A case study in the French Morvan
- 将圣诞树种植园识别为罕见目标分割问题，设计专用方法。
- 引入难例挖掘策略，使查准率-查全率曲线下面积从0.204升至0.913。
- 适用于遥感农业监测，尤其适合稀疏、密集种植场景。
圣诞树种植园具有经济价值，但在遥感领域仍属未充分探索的应用方向。其边界提取困难，原因包括高密度种植、短轮作周期、与周边植被视觉相似、仅有一个参考年份的密集标注数据，以及景观尺度上的严重类别不平衡。尽管深度学习在植被制图中表现优异，但现有方法多针对森林、通用林地或果园，未能专门应对圣诞树种植园的结构特性和强背景干扰。为此，本文提出三项贡献：（i）将圣诞树种植园制图视为一类独特的稀有目标语义分割任务；（ii）引入难例挖掘（HNM）策略以增强对混淆背景的区分能力；（iii）在监督测试、时间迁移和大范围验证等多个层面评估框架性能。在2020年独立测试集上，最佳模型DeepLabV3+ResNet-34取得0.733的交并比和0.846的F1分数。采用HNM后，查准率-查全率曲线下面积从0.204提升至0.913。时间迁移实验显示良好泛化性，2017/2018年达到0.751/0.858，2023年为0.691/0.817。大规模验证表明任务难度高，圣诞树种植园仅占总面积的1.72%（2017/2018年，1,498.4公顷）和2.04%（2023年，1,782.2公顷），总范围达87,309.4公顷。
原文摘要 · Abstract (English)
Christmas tree plantations are economically relevant, yet a largely unexplored application domain in Remote Sensing (RS). Their delineation is challenging because of high planting density, short rotation cycles, visual confusion with surrounding vegetation, the availability of dense labels for one reference year only, and severe class imbalance at the landscape scale. Although Deep Learning (DL) methods have shown strong potential for vegetation mapping, existing approaches are typically designed for forests, generic plantation systems, or orchards, and do not explicitly address the structural specificity and hard-negative confusion that characterize Christmas tree plantations. In response to these challenges, this work makes three main contributions: (i) it frames Christmas tree plantation mapping as a distinct rare-target semantic segmentation problem; (ii) it introduces a Hard Negative Mining (HNM) strategy to improve discrimination against confusing background patterns; and (iii) it evaluates the proposed framework across complementary levels, including supervised testing, temporal transfer, and large-scale validation. On the 2020 test set held out, the best model, DeepLabV3 with a ResNet-34 encoder, achieves an IoU of 0.733 and an F1-score of 0.846. HNM substantially improves precision-recall behavior, increasing the area under the precision-recall curve from 0.204 to 0.913. Temporal inference further shows meaningful transferability, reaching IoU/F1 values of 0.751/0.858 on 2017/2018 and 0.691/0.817 on 2023. Large-scale validation further highlights the intrinsic difficulty of the task, as Christmas tree plantations occupied only a very small fraction of the extent of the common evaluation, corresponding to 1,498.4 ha (1.72\%) in 2017/2018 and 1,782.2 ha (2.04\%) in 2023 out of 87,309.4 ha in total.
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