A multi-scale remote sensing semantic segmentation model with boundary enhancement based on UNetFormer

The precise execution of semantic segmentation on remote sensing data is a pivotal factor. It determines the achievements and impact of geoscience endeavors and their applications. However, challenges caused by target edge blurring and scale variability in high-resolution remote sensing imagery hinder the improvement of segmentation accuracy. In this work, to address these issues, a Boundary-Enhanced Multi-Scale Semantic Segmentation Network (BEMS-UNetFormer) based on UNetFormer is proposed for remote sensing data. Firstly, an improved Boundary Awareness Module (BAM) is used to extract the edge information of the target from the low-level features to enhance the recognition of the target edges. Secondly, the improved Boundary-Guided Fusion Module (BFM) incorporates the edge information from BAM into subsequent decoding, further refining the precise representation of boundary regions. Finally, at the pivotal junction between the encoder and decoder, the Multi-Scale Cascaded Atrous Spatial Pyramid Pooling (MSC-ASPP) is designed, capable of deeply mining and integrating multi-scale deep features. The method was tested on two mainstream datasets, Potsdam and Vaihingen, achieving 86.12% and 83.10% MIoU, respectively, improving by 1.38% and 1.79% over the baseline model. Notably, the IoU and F1 Score for the small-scale target "Car" in the Potsdam dataset reached 91.20% and 95.57%, respectively, while the "Building" and "LowVeg" categories in the Vaihingen dataset achieved the highest IoU and F1 Score. The experimental results indicate that the proposed method demonstrates higher precision in segmenting small-scale targets and target boundaries, surpassing mainstream methods overall.