标题： FSDENet：一种基于频率和空间域的细节增强网络用于遥感语义分割 显示英文标题

标题： FSDENet: A Frequency and Spatial Domains based Detail Enhancement Network for Remote Sensing Semantic Segmentation

摘要： 为了充分利用遥感图像分割中的空间信息并解决由于灰度变化（例如阴影和低对比度区域）引起的语义边缘模糊问题，我们提出了基于频率和空间域的细节增强网络（FSDENet）。 我们的框架采用空间处理方法来提取丰富的多尺度空间特征和细粒度语义细节。 通过在全局映射中使用快速傅里叶变换（FFT）有效地整合全局和频域信息，模型在灰度变化下的辨别全局表示的能力得到了显著加强。 此外，我们利用哈尔小波变换将特征分解为高频和低频成分，利用它们对边缘信息的不同敏感性来优化边界分割。 通过将空间粒度与频域边缘敏感性相结合，该模型实现了双域协同作用，显著提高了边界区域和灰度过渡区域的分割精度。 全面的实验结果表明，FSDENet在四个广泛采用的数据集：LoveDA，Vaihingen，Potsdam和iSAID上达到了最先进的（SOTA）性能。 显示英文摘要

摘要： 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.