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Rice Lodging Area Extraction Based on YCbCr Spatial and Texture Features

文献类型： 外文期刊

作者： Ding, Yang 1 ; Zhao, Xin 1 ; Zhang, Dongyan 1 ; Liang, Dong 1 ; Wang, Zhicun 1 ; Xi, Shangjing 1 ; Du, Shizhou 2 ;

作者机构： 1.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, Hefei 230601, Peoples R China

2.Anhui Acad Agr Sci, Inst Crops, Hefei 230031, Peoples R China

关键词： rice lodging; UAV; texture features; SVM; Kappa coefficient

期刊名称：2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)

ISSN： 2153-6996

年卷期： 2019 年

页码：

收录情况： SCI

摘要： Lodging is one of the important factors causing the decrease of rice yield and quality. For obtaining the lodging area of rice accurately, evaluating the disaster situation correctly, and providing technical support for agricultural insurance and production decision-making, this paper used the consumption-grade unmanned aerial vehicle DJI Phantom 4 Advanced to acquire high-resolution visible images at 100 m flight altitude, to study the extraction of rice lodging features and area based on machine vision technology. Firstly, through calculating four texture features such as the correlation, energy, contrast and homogeneity, it was found that the difference between lodging and normal rice was the most obvious by the texture image of homogeneity. Furthermore, on the basis of homogeneity texture, the YCBCR transform was applied to enhance texture image, namely as the R1G1B1 image. We found that the separability of samples was as high as 1.9 for G1 Band from the R1G1B1 image. The G1 Band was chosen as the most effective feature to distinguish lodging area from rice field. On this basis, the texture feature T of G1 band was obtained by choosing the texture of the deficit moment and setting the size of the sub-window to 29 x29_. Finally, the color feature, color feature plus texture feature T were used to classify by support vector machine (SVM), and the classification accuracy were 87.33% and 92.96%, respectively. The corresponding Kappa coefficients were respectively 0.658 and 0.817. The results showed that the combination of texture features can improve the recognition accuracy of visible image for lodging rice. Meanwhile, compared with the traditional K-means and maximum likelihood method, the SVM-based classification was the best. The above results provide valuable reference for the extraction of other crops' lodging area based on optical sensor.

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