Abstract: 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.