F2Net通过频域分解实现超分辨率遥感图像语义分割，兼顾细节与全局信息。
F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation
- 将图像分高低频处理，高频频段保细节，低频频段抓长程依赖。
- 在DeepGlobe和Inria数据集上分别达到80.22和83.39的mIoU，性能领先。
- 适合需要高精度遥感分割的应用，如城市规划与环境监测。
超分辨率（UHR）遥感图像的语义分割对环境监测和城市规划等应用至关重要，但面临计算与优化挑战。传统方法或因下采样丢失细节，或因分块处理破坏全局上下文。多分支网络虽缓解此矛盾，却存在计算效率低、训练时梯度冲突的问题。本文提出F2Net，一种频率感知框架，将UHR图像分解为高低频成分进行针对性处理：高频频段保留全分辨率结构细节，低频频段通过双子分支捕捉短程与长程依赖。混合频域融合模块结合两者输出，受两种新目标引导：跨频域对齐损失确保频率成分间语义一致，跨频域平衡损失调节各分支梯度幅度以稳定训练。在DeepGlobe和Inria Aerial基准上评估，F2Net分别取得80.22和83.39的mIoU，达到当前最优水平。代码将公开。
原文摘要 · Abstract (English)
Semantic segmentation of ultra-high-resolution (UHR) remote sensing imagery is critical for applications like environmental monitoring and urban planning but faces computational and optimization challenges. Conventional methods either lose fine details through downsampling or fragment global context via patch processing. While multi-branch networks address this trade-off, they suffer from computational inefficiency and conflicting gradient dynamics during training. We propose F2Net, a frequency-aware framework that decomposes UHR images into high- and low-frequency components for specialized processing. The high-frequency branch preserves full-resolution structural details, while the low-frequency branch processes downsampled inputs through dual sub-branches capturing short- and long-range dependencies. A Hybrid-Frequency Fusion module integrates these observations, guided by two novel objectives: Cross-Frequency Alignment Loss ensures semantic consistency between frequency components, and Cross-Frequency Balance Loss regulates gradient magnitudes across branches to stabilize training. Evaluated on DeepGlobe and Inria Aerial benchmarks, F2Net achieves state-of-the-art performance with mIoU of 80.22 and 83.39, respectively. Our code will be publicly available.
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