A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation

• URL: http://arxiv.org/abs/2506.19406v1
• Date: Tue, 24 Jun 2025 08:20:08 GMT
• Title: A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation
• Abstract summary: GLCANet is a lightweight segmentation framework designed for UHR remote sensing imagery.<n>A self-attention mechanism enhances long-range dependencies, refines global features, and preserves local details for better semantic consistency.<n>A masked cross-attention mechanism also adaptively fuses global-local features, selectively enhancing fine-grained details while exploiting global context to improve segmentation accuracy.
• Score: 1.833928124984226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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