基于自适应聚类的无人机高光谱树冠表型分割，实时高效。
Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data
- 采用在线增量聚类与轻量网络，动态优化计算与精度平衡。
- 相比传统方法，推理速度更快，叶绿素等生化指标预测更准。
- 适合资源受限的边缘设备部署，适用于农业遥感场景。
无人机（UAV）结合高光谱成像（HSI）可捕捉精细光谱信息，用于预测叶绿素、类胡萝卜素和花青素等不可见叶片属性，在环境与农业应用中具有潜力。然而，高光谱数据量大，对计算和存储资源有限的远程设备构成挑战。本文提出一种在线高光谱简单线性迭代聚类算法（OHSLIC），实现树冠表型的实时分割。OHSLIC通过自适应增量聚类和轻量神经网络，降低噪声与计算开销。研究构建了一个含真实叶片参数与光相互作用的定制仿真高光谱数据集。结果表明，相较于像素或窗口基方法，OHSLIC在回归精度和分割性能上均更优，且显著降低推理时间。其自适应聚类机制支持计算效率与准确性的动态权衡，为高光谱应用在边缘设备上的可扩展部署提供了可行路径。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) combined with Hyperspectral imaging (HSI) offer potential for environmental and agricultural applications by capturing detailed spectral information that enables the prediction of invisible features like biochemical leaf properties. However, the data-intensive nature of HSI poses challenges for remote devices, which have limited computational resources and storage. This paper introduces an Online Hyperspectral Simple Linear Iterative Clustering algorithm (OHSLIC) framework for real-time tree phenotype segmentation. OHSLIC reduces inherent noise and computational demands through adaptive incremental clustering and a lightweight neural network, which phenotypes trees using leaf contents such as chlorophyll, carotenoids, and anthocyanins. A hyperspectral dataset is created using a custom simulator that incorporates realistic leaf parameters, and light interactions. Results demonstrate that OHSLIC achieves superior regression accuracy and segmentation performance compared to pixel- or window-based methods while significantly reducing inference time. The method`s adaptive clustering enables dynamic trade-offs between computational efficiency and accuracy, paving the way for scalable edge-device deployment in HSI applications.
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