RESEARCH ON CROP INFORMATION EXTRACTION OF AGRICULTURAL UAV IMAGES BASED ON BLIND IMAGE DEBLURRING TECHNOLOGY AND SVM

Abstract

The blurring of crop images acquired by agricultural Unmanned Aerial Vehicle (UAV) due to sudden inputs by operators, atmospheric disturbance, and many other factors will eventually affect the subsequent crop identification, information extraction, and yield estimation. Aiming at the above problems, the new proposed combined deblurring algorithm based on the re-weighted graph total variation (RGTV) and L0-regularized prior, and the other two representative deblurring algorithms were applied to restore blurry crop images acquired during UAV flight, respectively. The restoration performance was measured by subjective vision, and objective evaluation indexes. The crop shape-related and texture-related feature parameters were then extracted, the Support Vector Machine (SVM) classifier with four common kernel functions was implemented for crop classification to realize the purpose of crop information extraction. The deblurring results showed that the proposed algorithm performed better in suppressing the ringing effect and preserving the image fine details, and retained higher objective evaluation indexes than the other two deblurring algorithms. The comparative analysis of different classification kernel functions showed that the Polynomial kernel function with an average recognition rate of 94.83% was most suitable for crop classification and recognition. The research will help in further popularization of crop monitoring based on UAV low-altitude remote sensing.