基于冻融循环的土壤物理状态的自动判别

摘要

In the present study, typical black soil in northeastern China was selected as the test object, and the simulated image processing was adopted combined with computerized tomography (CT) scanning.With the extracted image feature by gray level co-occurrence matrix and principal component analysis (PCA), the Euclidean distance between the feature vector of test image and verify image was calculated, which built the basis for automatic discrimination of different soil physical states caused by freeze-thaw.It was shown that it could realize automatic identification of different soil physical state by image features extracted by either gray level co-occurrence matrix or PCA.And the identification accuracy of gray-level co-occurrence matrix method was higher than that of principal component analysis method for the same soil CT tomography image database.%以东北典型黑土区土壤为研究对象,采用CT扫描技术与图像处理相结合的方法,通过灰度共生矩阵和主成分分析法提取图像特征,计算测试图像特征向量与训练图像特征向量间的欧氏距离,以此为依据,实现对经历不同冻融循环次数土壤的自动判别.研究结果表明:面向土壤CT图像数据库,基于灰度共生矩阵和主成分分析提取的图像特征,均能实现对土壤的自动判别,但灰度共生矩阵法的判别正确率要高于主成分分析法.