无人机可见光遥感和特征融合的小麦倒伏面积提取

倒伏是造成小麦减产和品质下降的主要原因之一。为快速准确地提取小麦倒伏面积，给农业保险理赔及灾后应急处置提供数据支持，该研究采用无人机遥感平台获取小麦倒伏后的冠层红绿蓝（Red-Green-Blue, RGB）可见光图像，并进行数字表面模型（Digital Surface Model，DSM）图像提取，计算了过绿植被（Excess Green, EXG）指数，利用ArcGIS中的镶嵌工具将不同图像特征进行融合，得到DSM+RGB融合图像和DSM+EXG融合图像，利用最大似然法和随机森林法对2种特征融合图像进行监督分类提取小麦倒伏面积，并与仅基于RGB可见光图像和DSM图像提取倒伏面积结果对比。结果表明，2种方法对4种图像进行小麦倒伏面积提取的整体趋势一致，且最大似然法提取效果整体优于随机森林法，基于最大似然法对RGB图像、DSM图像、DSM+RGB特征融合图像、DSM+EXG特征融合图像提取倒伏小麦面积的整体精度分别为77.21%、93.37%、93.75%和81.78%，Kappa系数分别为0.54、0.86、0.87和0.64，对比分析发现DSM+RGB特征融合图像提取小麦倒伏面积精度最高。该研究表明通过图像特征融合的方法能够有效提取倒伏小麦信息，为快速提取小麦倒伏面积提供参考。

Wheat lodging area extraction using UAV visible light remote sensing and feature fusion

Wheat is one of the main food sources in China, and wheat yield is very important to China''s food security. Lodging is a common agricultural disaster in wheat production. Lodging often causes changes in the population structure of wheat, affects the protein synthesis and nutrient transport of wheat, and greatly influences the quality and yield of wheat. To extract the lodging area of wheat quickly and accurately, this study adopted the Unmanned Aerial Vehicle (UAV) remote sensing platform to obtain the visible light images (including the red band, the green band, and the blue band) and the Digital Surface Model (DSM) images of the wheat canopy at the late stage of grain filling. The Excess Green (EXG) index was also calculated. Fusion images of DSM+RGB as well as fusion images of DSM+EXG were created by ArcGIS image mosaic tools. By using the maximum likelihood method and random forest method, the two feature fusion images were supervised and classified for extracting the wheat lodging area, and the results were also compared with the extracted results only based on the RGB images and the DSM images. The results of using four images to extract the lodging area of wheat were evaluated by the confusion matrix and Kappa coefficient. The results showed that the two methods had the same overall trend of wheat lodging area extraction on four images, and the extraction effect of the maximum likelihood method was generally excellent. Based on the random forest method, the overall accuracy of RGB image, DSM image, DSM+RGB fusion image, DSM+EXG fusion image to extract the lodging wheat area was 77.21%, 93.37%, 93.75%, 81.78%, and the Kappa coefficients were 0.54, 0.86, 0.87, 0.64, respectively. According to comparative analysis, the image data extraction accuracy after fusion of the DSM and RGB parameters was the highest, and the overall accuracy was up to 93.75%, the Kappa coefficient was 0.87. The difference between the two images was improved due to added elevation information. The extraction effect of the lodging area was the worst which was only based on the RGB images with an overall accuracy of 77.21% and a Kappa coefficient of 0.54. The reason was that the wheat with normal plants but yellow leaves were misclassified. Compared with the fusion images of DSM+RGB, the classification accuracy based on the DSM images alone was not much different, indicating that DSM effectively reflected the difference between lodging wheat and normal wheat. It was an effective feature, while the accuracy of the latter was higher than that of the former, indicating that the fusion of unreasonable image features effectively improved the image difference and the extraction accuracy of the lodging area of wheat. In this study, the fusion of different image features preserved more detailed information of the image, which enhanced the difference of image features and made the fused image suitable for image classification. This research had shown that the method of image feature fusion could effectively extract the information of lodging wheat and provide a method for accurate identification of wheat lodging.