基于可见光光谱和随机森林算法的冬小麦冠层图像分割

数字图像分析技术因其高效、快速等特点，已被广泛应用于作物长势和氮素营养状况的无损监测领域。获取作物冠层覆盖度及可见光光谱亮度值及其衍生的色彩指数，需要从作物冠层图像中准确分割出作物图像。研究以冬小麦与背景(土壤)在可见光波段反射率的差异为依据，利用基于CIEL*a*b*色彩空间a*分量的最大类间方差法和基于sRGB和CIEL*a*b*两个色彩空间的随机森林算法对冬小麦冠层图像进行了分割，并比较了图像分割精度。结果表明，三种方法均具有较高的分割精度，其中基于随机森林算法的图像分割效果明显好于最大类间方差法，而基于sRGB和CIEL*a*b*两个色彩空间的随机森林算法的图像分割效果差异较小。研究结果表明，随机森林算法可直接利用冠层图像可见光波段的三个色彩分量(R，G和B)分割冬小麦冠层图像。

Segmentation of Winter Wheat Canopy Image Based on Visual Spectral and Random Forest Algorithm

Digital image analysis has been widely used in non-destructive monitoring of crop growth and nitrogen nutrition status due to its simplicity and efficiency. It is necessary to segment winter wheat plant from soil background for accessing canopy cover, intensity level of visible spectrum (R, G, and B) and other color indices derived from RGB. In present study, according to the variation in R, G, and B components of sRGB color space and L*, a*, and b* components of CIEL*a*b* color space between wheat plant and soil background, the segmentation of wheat plant from soil background were conducted by the Otsu’s method based on a* component of CIEL*a*b* color space, and RGB based random forest method, and CIEL*a*b* based random forest method, respectively. Also the ability to segment wheat plant from soil background was evaluated with the value of segmentation accuracy. The results showed that all three methods had revealed good ability to segment wheat plant from soil background. The Otsu’s method had lowest segmentation accuracy in comparison with the other two methods. There were only little difference in segmentation error between the two random forest methods. In conclusion, the random forest method had revealed its capacity to segment wheat plant from soil background with only the visual spectral information of canopy image without any color components combinations or any color space transformation.