Authors

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.

Abstract in Chinese

由于人为的突然输入，大气扰动以及许多因素将会导致农用无人机获取的作物图像出现模糊现象，最终会影响后续的作物识别，信息提取以及产量估计。针对这个问题，采用一种新提出的基于重加权总变分（RGTV）结合L0正则化先验的图像盲复原算法以及其他两种较具有代表性的去模糊算法分别对无人机在作业时获取到的作物图像进行复原处理。采用主观以及客观评价指数对复原效果进行评价。采用带有四种常见核函数的支持向量机分类器用于作物分类从而实现作物信息提取的目的。复原结果表明提出的算法能很好地抑制振铃效应以及保留图像的细节，并有着比另外两种算法较高的客观评价指数上。对不同核函数的分析表明：多项式核函数用于94.83%的平均识别率是最适合作物的分类以及识别。研究将会帮助采用无人机低空遥感进行作物监测的推广提供一定的帮助。