Object-oriented crop classification based on UAV remote sensing imagery

UAV remote sensing images have the advantages of high spatial resolution, fast speed, strong real-time performance, and convenient operation, etc., and have become a recently developed, vital means of acquiring surface information. It is an important research task for precision agriculture to make full use of the spectrum, texture, color and other characteristic information of crops, especially the spatial arrangement and structure information of features, to explore effective methods for the classification of multiple varieties of crops. In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images, the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field, which mainly includes image segmentation and object classification. The results showed that the plots obtained after classification were continuous and complete, basically in line with the actual situation, and the overall accuracy of crop classification was 91.73%, with Kappa coefficient of 0.87. Compared with the crop planting area based on remote sensing interpretation and field survey, the area error of 17 species of crops in this study was controlled within 15%, which provides a basis for object-oriented crop classification of UAV remote sensing images.