Ablation experiments on the Vaihingen dataset.

As the spatial resolution of remote sensing imagery continues to be improved, the complexity of the information also increases. Remote sensing images generally have characteristics such as wide imaging ranges, dispersed distribution of similar land objects, complex boundary shapes, and dense small targets, which pose severe challenges to semantic segmentation tasks. To address these challenges, we propose a channel reconstruction and dual attention dynamic fusion network (CRDFNet), which is a semantic segmentation network for remote sensing image that can effectively integrate global and local contexts. To better handle complex boundary shapes, we designed a channel feature aggregation module (CFAM), which can extract spatially redundant information during feature fusion and enhance high-resolution detail features. Through a channel reconstruction block, it promotes the alignment of fine-grained information from the encoder with high-level semantic information from the decoder, effectively aggregating multi-scale features extracted by the encoder and significantly improving segmentation accuracy. At the same time, to optimize the segmentation performance of small targets, we propose a dual attention feature refinement module (DAFRM), which achieves precise segmentation of small targets by effectively fuses the shallow spatial features of the encoder and the deep semantic features of the decoder through a dynamic fusion mechanism guided by dual attention. Experimental results on the Potsdam, Vaihingen, UAVid, and MSIDBG datasets demonstrate that CRDFNet outperforms existing methods in terms of F1 score, OA, and mIoU (Intersection over Union), validating its excellent performance.随着遥感影像的空间分辨率持续提升，其蕴含的信息复杂度也同步增加。遥感影像通常具备成像范围广、同类地物分布分散、边界形状复杂以及小目标密集等特性，这给语义分割任务带来了严峻挑战。为应对上述挑战，我们提出了一种通道重构与双注意力动态融合网络（Channel Reconstruction and Dual Attention Dynamic Fusion Network，CRDFNet），该网络是一款面向遥感影像的语义分割模型，可有效融合全局与局部上下文信息。为更好地处理复杂边界形状问题，我们设计了通道特征聚合模块（Channel Feature Aggregation Module，CFAM），其能够在特征融合过程中提取空间冗余信息，并增强高分辨率细节特征。通过通道重构块，该模块可推动编码器输出的细粒度信息与解码器输出的高层语义信息实现对齐，有效聚合编码器提取的多尺度特征，显著提升分割精度。与此同时，为优化小目标的分割性能，我们提出了双注意力特征细化模块（Dual Attention Feature Refinement Module，DAFRM），该模块通过双注意力引导的动态融合机制，将编码器的浅层空间特征与解码器的深层语义特征进行有效融合，从而实现小目标的精准分割。在Potsdam、Vaihingen、UAVid以及MSIDBG数据集上开展的实验结果表明，CRDFNet在F1分数、总体精度（Overall Accuracy，OA）以及平均交并比（mean Intersection over Union，mIoU）指标上均优于现有方法，验证了其优异的分割性能。