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Quantification and characterizing of soil microstructure features by image processing technique

机译:通过图像处理技术的定量与结论土壤微观结构特征

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An analyzing program SMAS based on digital image processing technique is developed for quantifying soil microstructure. By using SMAS, a series of geometrical and morphological indexes of soil particles/pores in microscale can be quantitatively determined. Three examples of using SMAS to quantify the microstructure features are shown. The analyzing results indicate that the developed program can effectively identify the morphology of soil particle and pore and accurately extract the soil microstructure indexes. A classification criterion for particle shape category is proposed based on the obtained values of morphology ratio and roundness. Moreover, effects of magnification and observation area of SEM images on the quantitative analysis results are discussed. It is important to select an appropriate magnification and observation area can cover as much structural information as possible while with high imaging quality. A recommendation approach is to stitch several images with relatively high magnification to one large image for quantification. Moreover, performing multiple scans on different zones of interest and then making comparative analysis is also an effective way to reduce quantification errors in microstructure observation. The findings of this investigation would be valuable for improving the reliability of quantitative characterization of soil microstructure on the basis of SEM images.
机译:基于数字图像处理技术的分析程序SMA用于量化土壤微观结构。通过使用SMA,可以定量确定微观粒子中的土壤颗粒/孔的一系列几何和形态指标。示出了使用SMA量化微结构特征的三个例子。分析结果表明,发达的程序可以有效地识别土壤颗粒和孔的形态,并准确提取土壤微动图指数。基于所获得的形态学比和圆度来提出粒子形状类别的分类标准。此外,讨论了SEM图像对定量分析结果的倍率和观察区域的影响。重要的是选择适当的放大率和观察区域可以在具有高成像质量的同时尽可能多地覆盖结构信息。推荐方法是针对一个大幅度缝合几个图像以进行量化。此外,在不同的感兴趣区域执行多个扫描,然后进行比较分析也是减少微观结构观察中的量化误差的有效方法。本研究的调查结果对于提高SEM图像来提高土壤微观结构的定量表征可靠性是有价值的。

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