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Leaf moisture content measurement using polarized active imaging LiDAR

In this paper, a polarized active imaging technology was utilized for measuring the moisture content of the holly leaves. Polarization images of the holly leaves encoded with the polarization degree were acquired by the polarized active imaging LiDAR. The polarization degree of the holly leaves was used as a variable to generate a mapping model to reflect the moisture content. The polarization degrees were calculated from polarization images matched by the method of Speeded Up Robust Feature (SURF) algorithm and affine transformation. Images were denoised by using the multiple iterative median filter and the Lee filter. The actual moisture contents of the holly leaves were obtained by the method of moisture drying processing. The predictive accuracy and stabilities were utilized to establish an optimal mapping model by the method of statistical fitting. Experiments were carried out to validate the mapping model between the moisture content and the polarization degree of the holly leaves. The results show that the polarization degrees of the holly leaves are significantly positive-correlated with the moisture content rising from 45% to 75%. The relationship between the moisture content and the polarization degree of the holly leaves was presented with exponential model.