# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 1 Introduction  
High-resolution remote sensing images encapsulate crucial information, and the segmentation of buildings proves valuable in diverse applications, including urban growth forecasting [1], disaster management [2], traffic management [3], and map updating [4]. Traditional segmentation methods, including edge-based [5], threshold processing [6], and graph theory [7] approaches, often require manual intervention and suffer from limitations in parameter selection and feature extraction. The traditional feature extraction methods result in increased within-group variance and decreased between-group variance in the input image, leading to suboptimal classification results. In recent years, deep learning, especially Convolutional Neural Networks (CNNs), has become increasingly prominent in the analysis of remote sensing images. AlexNet [8] introduced a revolutionary approach to deep learning in applications, and subsequent advancements led to the development of various studies in remote sensing images, such as object detection [9; 10; 11; 12; 13], semantic segmentation [14; 15; 16], road extraction [18], and environmental protection [19]. Fully Convolutional Networks (FCNs) [20] addressed the limitationsof traditional CNNs by preserving spatial information and performing pixel-level inference. However, FCNs do not capture pixel relationships during upsampling, resulting in information loss and suboptimal performance at object boundaries. To overcome these limitations, researchers have developed improved variants based on FCN, including U-Net [21], HRNet [22], SegNet [23], etc. These networks tackle the challenge of uncorrelated feature information across layers and locations, as well as the inability to directly integrate feature maps at different locations. U-Net, proposed by [PERSON] et al. [21] has played a pivotal role in image segmentation. By combining information from downsampled shallow convolutional layers and employing skip connections, U-Net effectively retains crucial shallow information during the upsampling process, preserving both semantic and spatial details. Inspired by the symmetric structure of U-Net, [PERSON] et al. [23] introduced SegNet, adopting an encoder-decoder architecture. The encoder progressively decreases the size of the input feature map through pooling layers, while the decoder, symmetric to the encoder, recovers image details and feature map size using deconvolution operations. The exceptional performance of U-Net in image segmentation has stimulated extensive research, leading to the development of several outstanding networks. For example, MFU-Net [24] incorporates a novel multi-scale feature extraction module that captures shallow information from different scales, reducing the semantic gap in building segmentation by replacing U-Net's skip connections. [PERSON] et al. [25] introduced a DenseU-Net, a model that integrates a focal loss function (MFB_Focalloss) weighted by median frequency balance to improve the overall accuracy of small object class segmentation. However, these methods face challenges in effectively integrating global pixel information, leading to information loss, and they do not adequately utilize contextual information between different categories, resulting in confusion in the segmentation results and reduced between-group differences. For high-resolution remote sensing images, segmenting large targets poses significant challenges due to the complexity of scenes, as well as the influence of illumination and imaging angles. These challenges arise from two main factors: (1) Large within-group differences: Targets within the same category exhibit substantial variations in characteristics across different times and locations. For instance, in urban areas, different types of buildings such as neighborhood housing, shopping malls, and self-built houses possess distinct spectral characteristics due to the subjective influence of designers.