The accurate acquisition of farmland planting information is the basis of precision agriculture. Collecting remote sensing data via unmanned aerial vehicle (UAV) is a convenient method to obtain precision agricultural information because of the high spatiotemporal resolution and flexibility. A quadrotor UAV equipped with a SEQUOIA sensor (one multi-spectral sensor and one RGB lens) was operated over the Jingyuetan agricultural area with five land cover types on September 4, 2017, to investigate the equipment’s feasibility for crop identification. To evaluate the effects of different data and classification methods on the accuracy of crop type classification, three combinations were tested: MDC + Four (Mahalanobis distance classifiers based on four-band reflectance), MDC + VIs (Mahalanobis distance classifiers based on Vegetation Indices) and MLC + VIs (maximum likelihood classifiers based on Vegetation Indices). The accuracy of the different classification methods was 83.06% (MDC + Four), 89.17% (MDC + VIs) and 92.60% (MLC + VIs). The MLC + VIs scheme was the most accurate, as it could partially overcome the influence of shadow and flattened. Since the reflectivity of different bands varied, all kinds of objects on the ground could be distinguished. This result revealed that multi-spectral UAV technology has the potential to identify crop type at the sub-meter spatial resolution, with the MLC based on the VIs.

中文翻译：

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基于无人机的高空间分辨率多光谱图像在作物类型准确识别中的潜力评估

精准获取农田种植信息是精准农业的基础。无人机（UAV）遥感数据采集具有高时空分辨率和灵活性，是获取精准农业信息的便捷方法。2017 年 9 月 4 日，一架搭载 SEQUOIA 传感器（1 个多光谱传感器和 1 个 RGB 镜头）的四旋翼无人机在具有五种土地覆盖类型的净月潭农区上空运行，以研究该设备用于作物识别的可行性。为了评估不同数据和分类方法对作物类型分类准确性的影响，测试了三种组合：MDC + 四种（基于四波段反射率的马氏距离分类器），MDC + VIs（基于植被指数的马氏距离分类器）和 MLC + VIs（基于植被指数的最大似然分类器）。不同分类方法的准确率分别为83.06%（MDC+4）、89.17%（MDC+VIs）和92.60%（MLC+VIs）。MLC+VIs方案是最准确的，因为它可以部分克服阴影和扁平化的影响。由于不同波段的反射率不同，因此可以区分地面上的各种物体。该结果表明，多光谱无人机技术具有以亚米空间分辨率识别作物类型的潜力，MLC 基于 VI。MLC+VIs方案是最准确的，因为它可以部分克服阴影和扁平化的影响。由于不同波段的反射率不同，因此可以区分地面上的各种物体。该结果表明，多光谱无人机技术具有以亚米空间分辨率识别作物类型的潜力，MLC 基于 VI。MLC+VIs方案是最准确的，因为它可以部分克服阴影和扁平化的影响。由于不同波段的反射率不同，因此可以区分地面上的各种物体。该结果表明，多光谱无人机技术具有以亚米空间分辨率识别作物类型的潜力，MLC 基于 VI。