快速、高效地区分并剔除火龙果地块间的杂草是提高火龙果单株提取精度的关键所在。通过四旋翼无人机平台获取高空间分辨率可见光波段的影像,分析火龙果植株和杂草R、G、B通道的光谱特征,根据其影像像元亮度值(DN值)构建相近颜色差异植被指数(CCVI),通过最大类方差(OTSU)阈值分割、Majority/Minority分析和聚类空洞填补,并与VDVI、EXG、NGRDI等可见光波段指数作对比研究。结果表明:1) 对于杂草覆盖度较高甚至杂草全覆盖火龙果的地块,CCVI提取效果较好,而其余3种指数在杂草与植株共生的地块中分类精度较低;2) 3个研究感兴趣区域(ROI)内样本的提取总体精度为94.60%,Kappa系数为0.9417,测试样本的提取总体精度和Kappa系数分别为94.33%、0.9328,经验证,对于不同区域内生活环境相似的植株的提取精度趋近一致。结果证实: CCVI能从杂草中辨别火龙果植株并提取单株,提取效果较好,该方法可与VDVI、EXG、NGRDI等指数互补应用。

Quickly and efficiently distinguishing and eliminating weeds is one of the keys to improving the extraction accuracy of pitaya plants. In this study, a high-resolution aerial image is acquired using a four-rotor unmanned aerial vehicle (UAV) platform with a visible light lens. The spectral characteristics of pitaya plants and weeds in R, G and B channels are then analyzed, and the close color difference vegetation index (CCVI) is constructed based on the pixel digital number (DN) values. Through OTSU threshold segmentation, majority/minority analysis and cluster hole filling, the mainstream indices including VDVI, EXG, and NGRDI are compared with CCVI. Results show the following: 1) for the pitaya plant plot with a high or full coverage rate for weed, the CCVI extraction effect is better, whereas the other three indices have poor classification effect in the plot where weeds and plants coexist; 2) for the three research ROI samples, the overall average accuracy and the Kappa coefficient are 94.60%, 0.9417, respectively, and for the test sample, the overall extraction accuracy and the Kappa coefficient are 94.33% and 0.9328, respectively. Thus, it is verified that the extraction accuracy of plants with similar habitats in different regions is fairly similar. Results confirm that the CCVI can be used to identify and extract the individual pitaya plants from the weeds with the UAV remote sensing scheme, and its extraction effect is good. The proposed method can be applied in conjunction with VDVI, EXG, and NGRDI.