通过频域与空间域协同增强，提升遥感图像分割边界精度。
FSDENet: A Frequency and Spatial Domains based Detail Enhancement Network for Remote Sensing Semantic Segmentation
- 结合傅里叶变换与哈尔小波，融合频域与空间细节信息。
- 在四个数据集上达到当前最优分割性能，尤其改善阴影区边界模糊问题。
- 适合需要高精度边缘识别的遥感图像分析任务。
为充分挖掘遥感图像中的空间信息并解决灰度变化（如阴影、低对比度区域）导致的语义边界模糊问题，本文提出基于频域与空间域的细节增强网络FSDENet。该框架采用空间处理方法提取多尺度空间特征与细粒度语义细节；通过快速傅里叶变换（FFT）在全局映射中有效融合全局与频域信息，显著增强模型在灰度变化下的全局表征能力。同时，利用哈尔小波变换将特征分解为高低频成分，利用其对边缘的不同敏感性优化边界分割。通过空间粒度与频域边缘敏感性的双域协同，显著提升边界区域及灰度过渡区的分割精度。大量实验表明，FSDENet在LoveDA、Vaihingen、Potsdam和iSAID四个主流数据集上均达到当前最优（SOTA）性能。
原文摘要 · Abstract (English)
To fully leverage spatial information for remote sensing image segmentation and address semantic edge ambiguities caused by grayscale variations (e.g., shadows and low-contrast regions), we propose the Frequency and Spatial Domains based Detail Enhancement Network (FSDENet). Our framework employs spatial processing methods to extract rich multi-scale spatial features and fine-grained semantic details. By effectively integrating global and frequency-domain information through the Fast Fourier Transform (FFT) in global mappings, the model's capability to discern global representations under grayscale variations is significantly strengthened. Additionally, we utilize Haar wavelet transform to decompose features into high- and low-frequency components, leveraging their distinct sensitivity to edge information to refine boundary segmentation. The model achieves dual-domain synergy by integrating spatial granularity with frequency-domain edge sensitivity, substantially improving segmentation accuracy in boundary regions and grayscale transition zones. Comprehensive experimental results demonstrate that FSDENet achieves state-of-the-art (SOTA) performance on four widely adopted datasets: LoveDA, Vaihingen, Potsdam, and iSAID.
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