REU-Net: A Remote Sensing Image Building Segmentation Network Based on Residual Structure and the Edge Enhancement Attention Module

Abstract

Building segmentation from high-resolution remote sensing images plays a crucial role in cadastral measurement, ecological monitoring, urban planning, and other applications. To address the current challenges in building segmentation from high-resolution remote sensing images, this paper proposes an improved deep learning-based network—REU-Net(2EEAM). The network replaces traditional convolutional blocks in U-Net with Residual Structures, deepening the network and alleviating the issue of vanishing gradients. Additionally, it substitutes the direct skip connections with two Edge Enhancement Attention Modules (EEAMs), enhancing the network’s ability to extract building edge information. Furthermore, a hybrid loss function combining edge consistency loss and binary cross-entropy loss is used to train the network, aiming to improve segmentation accuracy. Experimental results show that REU-Net(2EEAM) achieves optimal performance across multiple evaluation metrics (such as P, MPA, MIoU, and FWIoU), particularly excelling in the accurate recognition of building edges, significantly outperforming other network models. This method provides a reliable foundation for the further optimization of building segmentation algorithms for remote sensing images.