A novel UNet segmentation method based on deep learning for preferential flow in soil

Abstract

Accurate binary images based on dye-tracing experiments are fundamental for understanding preferential flow in soil. Nevertheless, general image-processing software, such as Photoshop, Image-Pro Plus, and ImageJ, cannot automatically and accurately binarize dye-strained regions for the areas with uneven staining and blurred boundaries, causing errors in the subsequent analysis of preferential flow characteristics. Therefore, this study aimed to develop a novel UNet segmentation method based on a dual-scale attention residual module (DARM-UNet), to improve the segmentation accuracy of preferential flow images and assist in digital soil descriptions. The proposed method is a two-level nested U-structure, i.e., a dual-scale attention residual module (DARM) on the lowest level and the typical architecture of UNet at the top level. On the lowest level, the DARM obtained improved contextual information and expand the receptive field based on the attention mechanism and the dual-scale convolution, which was used to improve the recognition ability of uneven dye and low-contrast regions. At the top level, the typical architecture of the UNet model was followed to fuse feature maps of different resolutions, in which each stage is filled by a DARM block. Compared with general image processing software and deep learning methods, the DARM-UNet method showed higher preferential flow segmentation accuracies (95.71% and 93.29%), recall rates (91.91% and 89.29%), and harmonic means (92.72% and 91.82%) for the natural secondary and hazelnut shrub forests, respectively. The four preferential flow indicators indicated that the flow patterns of the natural secondary and hazelnut shrub forests varied with soil depth. In natural secondary forests, the preferential flow started from the surface and was of the primary flow type, whereas in hazelnut shrub forests, it occurred only in the deeper layer, following the matrix flow and concentrating in the 0–16 cm soil layer. This study demonstrated that the proposed DARM-UNet method could effectively identify and segment preferential flow of different forests, especially the areas with uneven staining and blurred boundaries.