发表机构
Helmholtz Centre for Environmental Research - UFZ（亥姆霍兹环境研究中心 - UFZ）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对树冠物候变化带来的挑战，开发自监督模型PhenoEmbed，在多光谱无人机时间序列基准上训练，通过对比和掩码重建目标学习树冠特征，实验表明其嵌入空间结构良好，能产生紧凑最近邻结构，为树冠表示学习及后续测试提供支持。
AI 中文摘要
树冠对于可靠的人工智能来说是一个具有挑战性的目标，因为它们不是静态物体，其光谱响应、内部纹理、透明度和明显边界在生长季节会发生显著变化。我们开发了PhenoEmbed，这是一种以树冠为中心的自监督时间嵌入模型，在HeideBench上通过对比和掩码重建目标进行训练，HeideBench是德国多劳尔海德森林树冠物候的18日期无人机多光谱时间序列基准。该模型将季节性树冠动态视为由叶片出现、树冠闭合、衰老和落叶条件驱动的物候外观变化。分割后的树冠多边形被保留为对象锚点，以便随时间提取对齐的以树冠为中心的作物，从而为每棵树学习一个总结季节性树冠外观的256维向量。在对5885个作物安全树冠的实验中，导出的嵌入显示出结构化的低维组织，前两个主成分解释了25.1%的方差，最近邻检索产生的中位数top-1余弦相似度为0.946。与手工制作的时间特征和学习的平均池化基线相比，PhenoEmbed产生了更紧凑的最近邻结构，消融实验表明对比损失、掩码重建损失和显式季节性时间特征各自影响学习到的嵌入空间的结构。这些结果支持PhenoEmbed作为一种可重复使用的森林树冠表示学习器，并激发了未来关于此类特征在季节性变化下是否能改善树级模型的下游测试。
英文摘要
Tree crowns are a challenging target for resilient AI because they are not static objects: their spectral response, internal texture, translucency, and apparent boundaries change substantially across the growing season. We develop PhenoEmbed, a self-supervised crown-centric temporal embedding model trained with contrastive and masked reconstruction objectives on HeideBench, an 18-date UAV multispectral time-series benchmark for forest crown phenology in D{ö}lauer Heide. The model treats seasonal crown dynamics as phenological appearance change driven by leaf emergence, canopy closure, senescence, and leaf-off conditions. Segmented tree crown polygons are retained as object anchors to extract aligned crown-centered crops through time, allowing one 256-dimensional vector summarizing seasonal crown appearance to be learned per tree. On 5,885 crop-safe crowns, the exported embeddings show structured low-dimensional organization, with the first two principal components explaining 25.1\% of variance and nearest-neighbor retrieval producing a median top-1 cosine similarity of 0.946. Compared with handcrafted temporal features and a learned mean-pooling baseline, PhenoEmbed yields substantially more compact nearest-neighbor structure, while ablations show that the contrastive loss, masked reconstruction loss, and explicit seasonal time features each affect the structure of the learned embedding space. These results support PhenoEmbed as a reusable forest crown representation learner and motivate future downstream tests of whether such features improve tree-level models under seasonal change.
