Pine Wilt Disease (PWD) is a forest infectious disease that inflicts substantial economic losses to China’s forestry. Its rapid spread and the significant challenges associated with its control make early detection of infected trees crucial for disaster prevention. Unmanned aerial systems (UASs) hyperspectral imaging (HSI) and light detection and ranging (LiDAR) technologies provide high-resolution spectral diagnostic information coupled with intricate three-dimensional structural data, which has potential for fine grained monitoring of PWD. However, how to fuse HSI and LiDAR data to identify the early infected individual trees is still a challenge. This study presents a novel instance segmentation network, BH3DNet, to identify individual trees at different PWD-infected stages by extracting high-level abstract features based on the fusion of drone HSI and LiDAR data. BH3DNet introduces the PointNet++ model as the base network, and incorporates a shared encoder and twin parallel decoders to align semantic category prediction and instance segmentation of individual trees in an end-to-end approach. By applying an enhanced point cloud dataset that fuses drone HSI and LiDAR point cloud data, this model facilitates the identification of PWD infection stages at the individual tree scale. We evaluated the proposed model in a Masson pine forest stand sparsely mixed with broadleaf trees in a variety of infection states ranging from healthy to severely infected by PWD, and compared the performance of the model using the RGB bands, full HSI bands and screened bands as inputs, respectively. BH3DNet achieves an overall accuracy of 89.65 % with a Kappa × 100 of 87.29 for identifying individual trees using screened HSI bands and LiDAR point cloud, significantly outperforming the Mask R-CNN using only HSI data (overall accuracy: 70.81 %, Kappa × 100: 64.16). Moreover, BH3DNet’s accuracy at the early infection stage reaches 83.75 %. It proves that fusing HSI and point cloud data reflects the information of individual trees distribution and infection status, and the BH3DNet is suitable for high-precision monitoring of PWD.

中文翻译：

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一种融合 UAS 高光谱图像和 LiDAR 数据识别马尾松松材线虫病的新型 BH3DNet 方法


松材线虫病（PWD）是一种森林传染病，给我国林业造成重大经济损失。它的快速传播和与其控制相关的重大挑战使得早期发现受感染的树木对于预防灾害至关重要。无人机系统 (UAS) 高光谱成像 (HSI) 和光探测与测距 (LiDAR) 技术提供高分辨率光谱诊断信息以及复杂的三维结构数据，具有对残疾人进行细粒度监测的潜力。然而，如何融合HSI和LiDAR数据来识别早期感染的单株树木仍然是一个挑战。本研究提出了一种新颖的实例分割网络 BH3DNet，通过基于无人机 HSI 和 LiDAR 数据的融合提取高级抽象特征来识别不同 PWD 感染阶段的个体树木。 BH3DNet 引入了 PointNet++ 模型作为基础网络，并结合了共享编码器和双并行解码器，以端到端的方法对齐各个树的语义类别预测和实例分割。通过应用融合无人机 HSI 和 LiDAR 点云数据的增强型点云数据集，该模型有助于识别单棵树尺度的 PWD 感染阶段。我们在马尾松林与阔叶树稀疏混合的各种感染状态（从健康到严重感染 PWD）中评估了所提出的模型，并使用 RGB 条带、完整 HSI 条带和筛选条带比较了模型的性能：分别输入。 BH3DNet 的总体准确率达到 89.65%，Kappa × 100 为 87。29 使用筛选的 HSI 波段和 LiDAR 点云识别单个树木，显着优于仅使用 HSI 数据的 Mask R-CNN（总体精度：70.81 %，Kappa × 100：64.16）。而且，BH3DNet在感染早期阶段的准确率达到83.75%。证明HSI和点云数据的融合反映了单株树木分布和感染状况的信息，BH3DNet适合对PWD进行高精度监测。