Because of the low resolution of CT images

it is difficult to quantize the loess microstructure accurately. Therefore

in this paper

SEM images are associated with triaxial CT images for the investigating of the microstructure of the collapsible loess. Firstly

the reasonable training samples are extracted from a large number of SEM images with different magnifications. The supervised classification of the CT images is carried out based on these training samples. For a comparison

supervised classification under gray classification of CT images is also made. The results show that: supervised classification of CT images based on SEM images exhibits better performance on the quantization monitoring of the change rule for loess microstructure. It is shown the high quality for monitoring larger pores

aggregates

clay aggregate particles and closed mineral substance of the loess sample during the triaxial shear test. It is believed that this supervised classification based on SEM images could provide a new sight for researching loess microstructure.