CAJ | 학술논문

该文使用2012年小型无人机遥感试验获取的红、绿、蓝彩色图像研究灌浆期玉米倒伏的图像特征和面积提取方法。研究首先计算和统计正常、倒伏玉米的30项色彩、纹理特征，然后比较特征的变异系数和相对差异评选出适宜区分正常、倒伏玉米的特征；通过分析发现，与红、绿、蓝色灰度比较，多项色彩、纹理特征的变异系数更大或不同类别间的相对差异更小，不适用于准确区分正常、倒伏玉米，最适于区分正常和倒伏玉米的特征是3项基于灰度共生矩阵的红、绿、蓝色均值纹理特征。分别基于色彩特征和评选出的纹理特征提取倒伏玉米面积，对比2种方法的误差发现，基于红、绿、蓝色均值纹理特征提取倒伏玉米面积的误差最小为0.3%，最大为6.9%，显著低于基于色彩特征提取方法的。该研究结果为应用无人机彩色遥感图像准确提取倒伏玉米面积提供了依据和方法。
해문사용2012년소형무인궤요감시험획취적홍、록、람채색도상연구관장기옥미도복적도상특정화면적제취방법。연구수선계산화통계정상、도복옥미적30항색채、문리특정，연후비교특정적변이계수화상대차이평선출괄의구분정상、도복옥미적특정；통과분석발현，여홍、록、람색회도비교，다항색채、문리특정적변이계수경대혹불동유별간적상대차이경소，불괄용우준학구분정상、도복옥미，최괄우구분정상화도복옥미적특정시3항기우회도공생구진적홍、록、람색균치문리특정。분별기우색채특정화평선출적문리특정제취도복옥미면적，대비2충방법적오차발현，기우홍、록、람색균치문리특정제취도복옥미면적적오차최소위0.3%，최대위6.9%，현저저우기우색채특정제취방법적。해연구결과위응용무인궤채색요감도상준학제취도복옥미면적제공료의거화방법。
The information of crop lodging, such as spatial distribution and area, is very critical for agricultural hazard assessment and agricultural insurance claims. It is hard work to measure the area of lodging in a ground survey. A survey method using remote sensing technology is fast and efficient, but it was limited by a lack of available satellite remote sensing data. In recent years, Unmanned Aerial Vehicle (UAV) has been rapidly developed in civil applications. A small UAV remote sensing system in which a UAV carries a digital camera is a portable, stable, and efficient tool for a crop field survey while there is no satellite remote sensing data, but only a few studies about a lodging survey using a UAV were published. There was no study of a survey of maize lodging using a RGB image. Therefore, the authors studied a survey method of maize lodging using some images derived from an UAV remote sensing experiment which was carried out in the Wan Zhuang agricultural high-tech industrial park of the Chinese Academy of Agricultural Sciences (Langfang City, Hebei Province of China) on Sept. 11th to 13th of 2012. In this experiment, some images of maize lodging were acquired after a lodging event on Sept. 12th of 2012. In this study, image features were calculated and summarized first. Three color features and 24 texture features were calculated by processing RGB images using HLS color transformation and co-occurrence texture filters. Mean, variance, coefficient of variation (CV), and relative difference (RD) of image features in normal and lodging maize were summarized. The optimum features for classification of normal and lodging maize were chosen from the 27 features by their coefficient of variation and relative difference. Finally, two methods of lodging area extraction, respectively based on RGB grey level and optimum features, were compared. The result of the image features summary showed that many features had a higher CV or lower RD compared to RGB grey levels, and were not suitable for classification of normal and lodging maize. According to CV and RD, three texture features, including the mean of red, the mean of green, and the mean of blue (RD:59.4%, 45.4%, 48.8%;CV of normal:10.6%, 7.9%, 8.0%;CV of lodging:7.5%, 5.6%, 7.2%), having a higher RD and a lower CV compared to a RGB grey level (RD:58.5%, 44.7%, 48.1%;CV of normal:20.1%, 16.2%, 21.3%;CV of lodging: 14.1%, 12.1%, 16.2%), are optimum indicators for the classification. Compared with measurements of a lodging area, the method based on these optimum classification features (0.3%, 3.5%,6.9%) had lower errors than the method based on a RGB grey level (22.3%, 94.1%, 32.0%). The shadow of a high plant might influence the precision of the classification, but the error is negligible. According to the results of these studies, we may safely draw the conclusion that the method to extract lodging maize area using RGB images of UAV remote sensing based on optimum texture features is accurate.