用区域注意力提升遥感图像多类别分割精度
EMRA-proxy: Enhancing Multi-Class Region Semantic Segmentation in Remote Sensing Images with Attention Proxy
- 基于区域级注意力机制，捕捉长距离上下文关系
- 在三个公开数据集上达到领先分割准确率
- 适合遥感图像精细分类与复杂场景分析
高分辨率遥感（HRRS）图像分割因空间布局复杂、目标外观多样而具有挑战性。虽然卷积神经网络擅长捕捉局部特征，但难以建模长程依赖；而变换器虽能建模全局上下文，却常忽略局部细节且计算开销大。我们提出一种新方法——区域感知代理网络（RAPNet），包含两个模块：上下文区域注意力（CRA）和全局类别精炼（GCR）。不同于传统基于网格的方法，RAPNet在区域层面操作，实现更灵活的分割。CRA模块利用变换器捕捉区域级上下文依赖，生成语义区域掩码（SRM）。GCR模块学习全局类别注意力图以精炼多类信息，结合SRM与注意力图实现精确分割。在三个公开数据集上的实验表明，RAPNet优于现有最先进方法，显著提升多类别分割准确率。
原文摘要 · Abstract (English)
High-resolution remote sensing (HRRS) image segmentation is challenging due to complex spatial layouts and diverse object appearances. While CNNs excel at capturing local features, they struggle with long-range dependencies, whereas Transformers can model global context but often neglect local details and are computationally expensive.We propose a novel approach, Region-Aware Proxy Network (RAPNet), which consists of two components: Contextual Region Attention (CRA) and Global Class Refinement (GCR). Unlike traditional methods that rely on grid-based layouts, RAPNet operates at the region level for more flexible segmentation. The CRA module uses a Transformer to capture region-level contextual dependencies, generating a Semantic Region Mask (SRM). The GCR module learns a global class attention map to refine multi-class information, combining the SRM and attention map for accurate segmentation.Experiments on three public datasets show that RAPNet outperforms state-of-the-art methods, achieving superior multi-class segmentation accuracy.
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