Liu Xiaoxia
School of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaYang Fengbao
School of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaWei Hong
School of Systems Engineering, University of Reading, Reading RG6 6AU, UKGao Min
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China1.School of Information and Communication Engineering, North University of China, Taiyuan 030051, China; 2.School of Systems Engineering, University of Reading, Reading RG6 6AU, UK
TP751
Aiming at the problem that the existing shadow detection algorithms are difficult to extract irregular and fragmented shadows in complex farmland scenes, a shadow detection algorithm for remote sensing images of farmland crops by UAV is proposed. Combining the color characteristics of the shadow/non-shadow area of the UAV image, construct a new gray scale transformation method based on dual-channel difference and G-band enhancement, and use the maximum between-class variance method to automatically threshold the grayscale image to obtain shadow detection result. Experiments with the data collected by the team at the National Corn Industry Technology System Experimental Demonstration Base show that the detection results of the proposed method are closer to real shadows, with an average overall accuracy of 0.9868 and an average F1 score of 0.9567.
