Pine wilt disease (PWD) poses a serious threat to the worldwide pine forest resources. Unmanned aerial vehicle (UAV) remote sensing has been widely used for PWD control, due to its flexibility and efficiency. Although pixel-level detection can obtain fine detection boundaries, there have been few related works in complex scenes because of the difficulty of setting a preset category system and the poor generalization. A preset category system establishes which categories are to be labeled, and is necessary for traditional pixel-level detection. However, the poor generalization leads to an obvious accuracy drop when detecting PWD in new scenes. In the proposed approach, to address the first issue, one-class classification (OCC) is introduced to detect diseased pixels, focusing only on the category of diseased pine trees. However, the numerous objects but low PWD pixel proportion makes the model optimization unbalanced, for which balanced unbiased detection risk estimation is proposed. To address the second issue, a novel model consisting of three-dimensional (3D) convolutional layers and transformer blocks is proposed to extract more robust features. A novel PWD detection framework based on deep OCC is finally proposed to achieve fine pixel-level PWD detection results. PWD detection experiments were conducted on eight UAV H² (high spatial and spectral resolution) image strips. In total, 300 PWD samples from strip 1 (accounting for roughly 0.009 % of the total pixels) and 400 unlabeled pixels formed the training set. The test experiments were conducted in the remaining seven strips to validate the model generalization. Satisfactory quantitative results (F1-score greater than 0.9) were obtained for all the test strips. The results indicate that the proposed method has a powerful ability to detect PWD in pine trees, even when the PWD proportion is low, and shows better model generalization than the traditional pixel-level detection methods.

松枯萎病（PWD）对全球松林资源构成严重威胁。无人机（UAV）遥感由于其灵活性和效率而被广泛用于PWD控制。像素级检测虽然可以获得精细的检测边界，但由于难以设置预设的类别系统，泛化性差，在复杂场景下相关工作较少。预设的类别系统确定要标记哪些类别，这对于传统的像素级检测是必要的。然而，较差的泛化导致在新场景中检测 PWD 时的准确率明显下降。在所提出的方法中，为了解决第一个问题，引入了一类分类（OCC）来检测患病像素，仅关注患病松树的类别。然而，物体数量多但PWD像素比例低导致模型优化不平衡，为此提出了平衡无偏检测风险估计。为了解决第二个问题，提出了一种由三维 (3D) 卷积层和变换器块组成的新模型，以提取更稳健的特征。最终提出了一种基于深度 OCC 的新型 PWD 检测框架，以实现精细的像素级 PWD 检测结果。PWD检测实验在八架无人机H上进行 最终提出了一种基于深度 OCC 的新型 PWD 检测框架，以实现精细的像素级 PWD 检测结果。PWD检测实验在八架无人机H上进行 最终提出了一种基于深度 OCC 的新型 PWD 检测框架，以实现精细的像素级 PWD 检测结果。PWD检测实验在八架无人机H上进行²（高空间和光谱分辨率）图像条。总共有来自条带 1 的 300 个 PWD 样本（约占总像素的 0.009%）和 400 个未标记的像素构成了训练集。在剩余的七个条带中进行了测试实验，以验证模型的泛化性。所有测试条都获得了令人满意的定量结果（F1 分数大于 0.9）。结果表明，即使在PWD比例较低的情况下，所提出的方法对松树PWD的检测能力也很强，并且比传统的像素级检测方法具有更好的模型泛化能力。