Semi-supervised segmentation of multi-scale soil pores based on a novel receptive field structure

Abstract

The study of soil pores using computed tomography (CT) technology and deep learning has been proven to be effective. However, conventional neural networks struggle to accurately segment both large connected pores and small scatter pores in soil CT images at different scales. To address this limitation, this paper proposes a semi-supervised multi-scale segmentation method (SMS). The SMS method comprises two parts: 1) Upstream multi-scale pore segmentation branches that generate multi-feature pore segmentation maps based on a novel receptive field structure, and 2) Downstream semi-supervised classification task that selects the optimal pore feature map for output. Experimental results demonstrate that SMS effectively enhances segmentation performance for multi-scale soil pores, achieving the highest accuracy, precision, recall, and F1-score values of 99.55 %, 83.76 %, 96.12 %, and 89.35 %, respectively. These results outperform four common deep learning methods and three traditional image processing software. This study represents a significant advancement in high-precision automated segmentation of soil pores, providing valuable image processing support for investigating soil structure characteristics, soil conservation, and soil ecosystem services.