基于无人机的高光谱成像对松材枯萎病个体的智能识别

摘要：松木线虫(PWN;木疏叶蝉(Bursaphelenchus xylophilus)是中国主要的外来入侵物种，由于缺乏天敌和极快的感染和传 播速度，对中国造成了巨大的经济和生态破坏。准确监测松材萎蔫病是及时有效防治松材萎蔫病的前提。无人机可携带 高光谱传感器进行近地遥感观测，可获得丰富的空间和光谱信息，具有识别感染树的潜力。深度学习技术可以利用丰富 的多维数据挖掘深度特征，以实现分类和目标识别等任务。因此，我们提出了一种改进的Mask R-CNN实例分割方法和 一种原型网络分类模型与个体树分割算法相结合的集成方法，验证深度学习模型和无人机高光谱图像识别 PWD 不同阶 段感染个体树的可能性。结果表明，两种方法对 PWD 的识别都取得了较好的效果:以筛选的频带作为输入数据，改进的 Mask R-CNN 的总体准确率为 71%，结合原型网络分类模型和单树分割的集成方法，基于筛选的频带数据，总体准确率 为 83.51%，其中对早期感染松树的识别准确率为 74.89%。本研究表明，改进的 Mask R-CNN 和集成原型网络方法对于 利用无人机高光谱数据识别 PWD 感染单株树是有效和实用的，所提出的集成原型网络能够实现 PWD 的早期识别，为 PWD的早期监测和控制提供新的技术指导。

关键词：无人机、高光谱影像、松树枯萎病、Mask R-CNN、原型网络

期刊：Remote Sens. 2023, 15, 3295.

DOI：https://DOI.org/10.3390/rs15133295

设备：GaiaSky

Intelligent Identification of Pine Wilt Disease Infected Individual Trees Using UAV-Based Hyperspectral Imagery

Abstract：The pine wood nematode (PWN; Bursaphelenchus xylophilus) is a major invasive species in China, causing huge economic and ecological damage to the country due to the absence of natural enemies and the extremely rapid rate of infection and spread. Accurate monitoring of pine wilt disease (PWD) is a prerequisite for timely and effective disaster prevention and control. UAVs can carry hyperspectral sensors for near-ground remote sensing observations, which can obtain rich spatial and spectral information and have the potential for infected tree identification. Deep learning techniques can use rich multidimensional data to mine deep features in order to achieve tasks such as classification and target identification. Therefore, we propose an improved Mask R-CNN instance segmentation method and an integrated approach combining a prototypical network classification model with an individual tree segmentation algorithm to verify the possibility of deep learning models and UAV hyperspectral imagery for identifying infected individual trees at different stages of PWD. The results showed that both methods achieved good performance for PWD identification: the overall accuracy of the improved Mask R-CNN with the screened bands as input data was 71%, and the integrated method combining prototypical network classification model with individual tree segmentation obtained an overall accuracy of 83.51% based on the screened bands data, in which the early infected pine trees were identified with an accuracy of 74.89%. This study indicates that the improved Mask R-CNN and integrated prototypical network method are effective and practical for PWD-infected individual trees identification using UAV hyperspectral data, and the proposed integrated prototypical network enables early identification of PWD, providing a new technical guidance for early monitoring and control of PWD.

Keywords：UAV; hyperspectral imagery; pine wilt disease; Mask R-CNN; prototypical networ