用多视角图像提升橡树落叶程度的精准识别
A novel ordinal multi-view aggregation scheme for oak defoliation
- 设计多视角集成模型，融合树冠与正南北向图像
- 三视角集成在所有指标上表现最佳，准确率显著提升
- 适合需要客观评估森林健康的科研与林业工作者
由气候和生物胁迫引发的森林退化威胁生态系统功能，精准监测树木健康至关重要。本文将树木落叶程度估计建模为有序分类问题，利用地面影像数据提出一种新型多视角集成框架，通过在不同视角（北、南、冠部）训练的卷积神经网络（CNN）聚合预测结果。该方法在保持模型一致性的同时，充分利用互补视觉信息。通过对比多种有序分类方法并分析各视角及其组合的贡献，结果表明：考虑落叶等级有序结构的方法优于名义分类；所提多视角集成始终优于单视角或两视角配置。尤其三视角集成在所有评估指标上均表现最优且最稳定。研究证实，结合深度学习（DL）、有序分类（OC）与多视角聚合，可实现对地中海疏林等复杂生态系统中森林健康状况的可扩展、一致且客观评估。
原文摘要 · Abstract (English)
Forest decline driven by climate and biotic stressors threatens ecosystem functioning, making accurate monitoring of tree health essential. In this work, we address tree defoliation estimation as an ordinal classification problem using ground-level imagery. We propose a novel multi-view ensemble framework that aggregates predictions from Convolutional Neural Networks (CNNs) trained on different perspectives of individual trees (north, south, and crown). This approach leverages complementary visual information while preserving modelling consistency through a homogeneous ensemble design. A comprehensive evaluation is conducted by comparing multiple ordinal classification methods and analysing the contribution of each view and their combinations. Results show that modelling the ordinal structure of defoliation levels improves performance over nominal approaches, while the proposed multi-view ensemble consistently outperforms single-view and pairwise configurations. In particular, the three-view ensemble achieves the most robust and accurate predictions across all evaluation metrics. These findings highlight the potential of combining Deep Learning (DL), Ordinal Classification (OC), and multi-view aggregation for scalable, consistent, and objective forest health assessment in complex ecosystems such as Mediterranean dehesas.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。