Zhang Shuzhe, Chen Taoyi, Su Fei, Xu Hao, Li Yan, Liu Yaohui
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China.
The 54th Research Institute of CETC, 589 Zhongshan West Road, Shijiazhuang 050081, China.
Sensors (Basel). 2025 Apr 20;25(8):2608. doi: 10.3390/s25082608.
Deep learning has been widely applied in building segmentation from high-resolution remote sensing (HRS) images. However, HRS images suffer from insufficient complementary representation of target points in terms of capturing details and global information. To this end, we propose a novel building segmentation model for HRS images, termed C_ASegformer. Specifically, we design a Deep Layered Enhanced Fusion (DLEF) module to integrate hierarchical information from different receptive fields, thereby enhancing the feature representation capability of HRS information from global to detailed levels. Additionally, we introduce a Triplet Attention (TA) Module, which establishes dependency relationships between buildings and the environment through multi-directional rotation operations and residual transformations. Furthermore, we propose a Multi-Level Dilated Connection (MDC) Module to efficiently capture contextual relationships across different scales at a low computational cost. We conduct comparative experiments with several state-of-the-art models on three datasets, including the Massachusetts dataset, the INRIA dataset, and the WHU dataset. On the Massachusetts dataset, C_ASegformer achieves 95.42%, 85.69%, and 75.46% for OA, F1score, and mIoU, respectively. C_ASegformer shows more accurate performance, demonstrating the validity and sophistication of the model.
深度学习已广泛应用于从高分辨率遥感（HRS）图像中进行建筑物分割。然而，HRS图像在捕捉细节和全局信息方面存在目标点互补表示不足的问题。为此，我们提出了一种用于HRS图像的新型建筑物分割模型，称为C_ASegformer。具体来说，我们设计了一个深度分层增强融合（DLEF）模块，以整合来自不同感受野的分层信息，从而增强HRS信息从全局到细节层面的特征表示能力。此外，我们引入了一个三重注意力（TA）模块，该模块通过多方向旋转操作和残差变换建立建筑物与环境之间的依赖关系。此外，我们提出了一个多级扩张连接（MDC）模块，以低计算成本有效捕捉不同尺度之间的上下文关系。我们在包括马萨诸塞数据集、INRIA数据集和WHU数据集在内的三个数据集上与几个最先进的模型进行了对比实验。在马萨诸塞数据集上，C_ASegformer的总体精度（OA）、F1分数和平均交并比（mIoU）分别达到95.42%、85.69%和75.46%。C_ASegformer表现出更准确的性能，证明了该模型的有效性和先进性。
