1. Institute of Surveying and Mapping, Guilin University of Technology, Guilin 541006, China 2. Institute of Civil and Architectural Engineering, Guilin University of Technology, Guilin 541006, China 3. Institute of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China
In order to explore the recognition accuracy of remote sensing technology of low-altitude UAV for surface features in agricultural areas with different forms under karst landform conditions, the authors chose three agricultural areas (each having an area size of 200 m×200 m) in Guilin City as the research object. Supported by UAV aerial images and ground survey data, the image analysis technology based on pixel and object-oriented was combined with support vector machine (SVM) algorithm, respectively, to build the remote sensing recognition model of agricultural areas under different geomorphological conditions, and the precision was comparatively studied and analyzed. The results show that the object-oriented SVM classification results retain the rough outline of the original ground features, and the plot is relatively complete, and hence this means is more suitable for the recognition of ground features in agricultural areas under karst landform conditions. Compared with the pixel based SVM classification method, the overall accuracy is higher by 6.54% , and the Kappa coefficient is higher by 0.135 . The SVM classification method based on pixel is suitable for feature recognition in agricultural areas with regular feature distribution. Compared with the object-oriented SVM classification method, the overall accuracy is higher by 2.92% and the Kappa coefficient is higher by 0.026 .
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Peiqing LOU, Xiaoyu CHEN, Shutong WANG, Bolin FU, Yongyi HUANG, Tingyuan TANG, Ming LING. Object recognition of karst farming area based on UAV image: A case study of Guilin. Remote Sensing for Land & Resources, 2020, 32(1): 216-223.
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2020, Vol. 32 
), 黄永怡1, 唐廷元1, 凌铭1
