# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 1 Introduction
Similarly, farming land on plains and in mountainous regions display different spectral properties, despite both falling under the farming land category. (2) Limited between-group distinction: Different categories often lack distinct features, leading to potential confusion during segmentation. For example, roads in urban areas can be easily mistaken for building shadows, while gardens surrounding houses may be erroneously classified as buildings. These factors contribute to the difficulty of accurately determining the category information of object boundary pixels, resulting in blurred boundaries and inaccurate target segmentation. The simple superposition convolution method faces challenges in obtaining precise category information for object boundaries and fail to achieve accurate target segmentation. To solve the above problems, researchers have turned to the attention mechanism and developed numerous innovative image segmentation models, building upon the strong segmentation performance of U-Net. Attention U-Net [26] proposed the attention gate (AG) model, which automatically learns to concentrate on target structures of varied shapes and sizes. It suppresses irrelevant regions in the input image while accentuating features crucial for a specific task. The multiscale attention module in MACU-Net [27] can unify the semantic features contained in low-level and high-level feature maps at different scales. MANet [28] extracts contextual dependencies through multiple effective attention modules. UNet-scAG [29] proposes a spatial channel attention gate (scAG) that addresses the common limitation of insufficient contextual information extracted by skip connections. MAResU-Net [30] improved skip connections for U-Net based on the linear attention mechanism (LAM) and residual network. [PERSON] et al. [31] introduced an innovative attention gate module (AG) that modifies the position of the \"Resampler\" in the attention gate to a Sigmoid function. This adjustment aims to bridge the gap between high-level and low-levelfeatures. They further presented AGs-Unet, a novel attention-based UNet architecture. [PERSON] et al. [32] proposed the holistic nested attentional U-Net (HA U-Net). It incorporates attention mechanisms and multi-scale nested modules for supervised prediction, enhancing the extraction of edge details in high-resolution remote sensing image segmentation tasks. However, the existing attention models proposed for the remote sensing image building segmentation task still still encounter challenges in preserving fine-grained details and maintaining sharp boundaries in the segmentation results. Particularly, when confronted with more complex scenes that involve additional elements such as cars, parking spaces, hay fields, sand, tree shadows, and other interferences, the extraction of accurate building information becomes insufficient, leading to numerous incorrect segmentations. Therefore, in this paper, we introduce a novel attention module named Dual Hybrid Attention (DHA) and propose the corresponding architecture, DHAU-Net, which is specifically tailored for building segmentation in High-Resolution Remote Sensing Images (HRRSI). The primary contributions of this study are outlined as follows. 1. Taking advantage of the attention mechanism in both the channel and spatial domains, this paper designs a novel feature extraction module named DHA. DHA combines the channel and spatial attention, processing feature maps separately through distinct attention mechanisms. This effectively harnesses the strengths of each attention mechanism, boosting the information screening capability. The result is a more precise and comprehensive feature extraction. 2. We introduced DHAU-Net by enhancing the skip connection approach in U-Net to tackle the disparities between lower-level and higher-level feature maps. In U-Net, lower-level feature maps contain more detailed spatial location information but weaker semantic details, while higher-level feature maps have stronger semantic information but less precise spatial location details.