Considering the fragmentation of karst mountainous areas, a Dajiang Innovation Mavic 2 Pro quadcopter drone was used to collect visible light images of a modern intensive agricultural planting area in response to the high labor intensity and low efficiency of manual assessment of agricultural plastic film mulching (PFM). First, the characteristics of RGB values of 10 main types of features in the sample area, such as PFM, crops, buildings, and roads, were analyzed based on the color index, geometric size, and texture characteristics. Second, based on the construction principle and form of the excess green index, we constructed the excess blue index (ExB), which comprehensively utilized the three visible light bands of red, green, and blue. By calculating the ExB of the image, the characteristics of the PFM were enhanced. Third, Gaussian High-Pass Filter (GHPF) was used for ExB images to retain high-frequency information of PFM targets and reduce the influence of noisy ground types such as plant ashes, blue-roofed buildings, asphalt roads, and water on identifying PFM. Then, combined with field measurements of PFM data, the GHPF images were segmented using Otsu and color slices. The polygon area method was used to eliminate polygons that were too large or too small. Combined with random samples from field surveys, the threshold segmented PFM polygons were verified. The accuracies of the PFM area by methods of Otsu and human–computer interaction threshold segmentation (HC-ITS) are 85.44% and 97.36%, respectively, and the average accuracies of the verification areas B-I and B-II are 90.03% and 82.32%, respectively. It is proved that the PFM ExB proposed in this study has good applicability. We find that the HC-ITS method is suitable for scenes with complex ground object types, and the Otsu method is suitable for scenes with fewer ground object types.

中文翻译：

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基于无人机可见光遥感的喀斯特破碎耕地农地膜覆盖提取

考虑到喀斯特山区的碎片化，针对农用地膜覆盖人工评估劳动强度大、效率低的问题，采用大疆创新Mavic 2 Pro四轴飞行器采集现代集约化农业种植区可见光图像。 ）。首先，基于颜色指数、几何尺寸和纹理特征，分析了样本区域内PFM、作物、建筑物、道路等10种主要特征类型的RGB值特征。其次，根据超绿指数的构建原理和形式，我们构建了超蓝光指数（ExB），它综合利用了红、绿、蓝三个可见光波段。通过计算图像的ExB，增强了PFM的特性。第三，ExB图像使用高斯高通滤波器（GHPF）保留PFM目标的高频信息，减少植物灰烬、蓝顶建筑、柏油路和水等噪声地面类型对识别PFM的影响。然后，结合 PFM 数据的现场测量，使用 Otsu 和彩色切片对 GHPF 图像进行分割。多边形面积法用于消除过大或过小的多边形。结合实地调查的随机样本，验证了阈值分割的 PFM 多边形。Otsu和人机交互阈值分割（HC-ITS）方法对PFM区域的准确率分别为85.44%和97.36%，验证区域BI和B-II的平均准确率分别为90.03%和82.32% ， 分别。证明本研究提出的 PFM ExB 具有良好的适用性。我们发现 HC-ITS 方法适用于地物类型复杂的场景，而 Otsu 方法适用于地物类型较少的场景。