轻量级网络提升超分辨率遥感图像分割精度与效率
A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation
- 双流结构融合全局语义与局部细节
- 跨注意力机制显著提升小目标分割准确率
- 适合资源受限场景下的实时遥感分析
随着超分辨率遥感技术的快速发展，对精确高效语义分割的需求日益增长。现有方法在计算效率和多尺度特征融合方面面临挑战。为此，本文提出GLCANet（Global-Local Cross-Attention Network），一种专为超分辨率遥感影像设计的轻量化分割框架。GLCANet采用双流架构，高效融合全局语义与局部细节，同时最小化显存占用。自注意力机制增强长程依赖关系，优化全局特征并保留局部细节，提升语义一致性。掩码交叉注意力机制自适应融合全局-局部特征，选择性强化细粒度信息，利用全局上下文提高分割精度。实验表明，GLCANet在准确率和计算效率上均优于现有先进方法。模型可高效处理大尺寸高分辨率图像，内存开销小，为真实遥感应用提供了有前景的解决方案。
原文摘要 · Abstract (English)
With the rapid development of ultra-high resolution (UHR) remote sensing technology, the demand for accurate and efficient semantic segmentation has increased significantly. However, existing methods face challenges in computational efficiency and multi-scale feature fusion. To address these issues, we propose GLCANet (Global-Local Cross-Attention Network), a lightweight segmentation framework designed for UHR remote sensing imagery.GLCANet employs a dual-stream architecture to efficiently fuse global semantics and local details while minimizing GPU usage. A self-attention mechanism enhances long-range dependencies, refines global features, and preserves local details for better semantic consistency. A masked cross-attention mechanism also adaptively fuses global-local features, selectively enhancing fine-grained details while exploiting global context to improve segmentation accuracy. Experimental results show that GLCANet outperforms state-of-the-art methods regarding accuracy and computational efficiency. The model effectively processes large, high-resolution images with a small memory footprint, providing a promising solution for real-world remote sensing applications.
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