RTAS-Net：一个用于遥感图像的ResNet转换器ASPP语义分割网络
RTAS-Net: A ResNet-transformer-ASPP semantic segmentation network for remote sensing images.
摘要(中文译文)
遥感图像语义分割面临显著的尺度变化和复杂的类别空间分布，这往往导致大区域语义不连续和小目标细节丢失。为解决这些问题，本文提出了一种基于U-Net的遥感语义分割网络RTAS-Net（ResNet-Transformer-ASPP分割网络），通过多尺度上下文聚合、细尺度强化和局部到全局建模的协同设计增强特征表示。在高语义层级，该网络引入ASPP聚合多尺度上下文信息并扩大有效感受野，同时集成Swin Transformer的窗口自注意力机制建模跨区域依赖关系，从而提升大尺度区域的语义一致性。在高分辨率跳跃连接处，嵌入轻量级mini-ASPP以强化并预融合细尺度邻域信息，并引入MobileViT增强局部纹理和细粒度结构表示，从而提升小目标的识别和边界刻画能力。RTAS-Net并非模块的简单堆叠，而是通过协调的跨层级路径实现全局语义与局部细节的统一建模。在ISPRS Potsdam、Vaihingen和LoveDA数据集上的实验结果表明，所提方法在mIoU、mF1和OA上均取得一致性提升，并进一步对参数规模和推理效率进行了全面分析，验证了其有效性和实际适用性。
Abstract
Semantic segmentation of remote sensing images faces pronounced scale variation and complex class-wise spatial distributions, which often lead to semantic discontinuity in large regions and the loss of fine details for small objects. To address these issues, this paper proposes a U-Net-based remote sensing semantic segmentation network termed RTAS-Net (ResNet-Transformer-ASPP Segmentation Network), which enhances feature representation through a collaborative design of multi-scale context aggregation, fine-scale reinforcement, and local-to-global modeling. At the high-semantic level, the network incorporates ASPP to aggregate multi-scale contextual information and enlarge the effective receptive field, while integrating the window-based self-attention mechanism of Swin Transformer to model cross-region dependencies, thereby improving semantic consistency over large-scale areas. At high-resolution skip connections, a lightweight mini-ASPP is embedded to reinforce and pre-fuse fine-scale neighborhood information, and MobileViT is introduced to strengthen local texture and fine-grained structural representations, thus enhancing the recognition and boundary delineation of small objects. Rather than a simple stacking of modules, RTAS-Net achieves unified modeling of global semantics and local details through coordinated cross-level pathways. Experimental results on the ISPRS Potsdam, Vaihingen and LoveDA datasets demonstrate that the proposed method achieves consistent improvements in mIoU, mF1, and OA, and further provides a comprehensive analysis of parameter scale and inference efficiency, validating its effectiveness and practical applicability.
如何引用
AMA
Ziheng Wang, Yang Li, Kejia Ma. RTAS-Net: A ResNet-transformer-ASPP semantic segmentation network for remote sensing images.. PloS one. 2026; doi:10.1371/journal.pone.0343729.
APA
Ziheng Wang, Yang Li, Kejia Ma (2026). RTAS-Net: A ResNet-transformer-ASPP semantic segmentation network for remote sensing images.. PloS one. https://doi.org/10.1371/journal.pone.0343729
GB/T 7714
Ziheng Wang, Yang Li, Kejia Ma. RTAS-Net: A ResNet-transformer-ASPP semantic segmentation network for remote sensing images.[J]. PloS one, 2026 doi:10.1371/journal.pone.0343729.
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