标题： 用于遥感图像语义分割的自适应频率增强网络 显示英文标题

标题： Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation

摘要： 高分辨率遥感图像的语义分割在土地利用监测和城市规划中起着至关重要的作用。基于深度学习的方法最近取得了显著进展，使得生成令人满意的分割结果成为可能。然而，现有方法在适应各种地表覆盖分布的网络参数调整以及增强空间域特征与频率域特征之间的交互方面仍面临挑战。为了解决这些挑战，我们提出了自适应频率增强网络（AFENet），它集成了两个关键组件：自适应频率与空间特征交互模块（AFSIM）和选择性特征融合模块（SFM）。AFSIM 根据输入图像的内容动态分离和调节高低频特征。它自适应地生成两个掩码以分离高频和低频成分，从而为地物特征表示提供最佳细节和上下文补充信息。SFM 选择性地融合全局上下文和局部详细特征以增强网络的表达能力，因此进一步增强了频率特征与空间特征之间的交互。在三个公开可用的数据集上的大量实验表明，所提出的 AFENet 超过了最先进的方法。此外，我们还验证了 AFSIM 和 SFM 在管理多种地表覆盖类型和复杂场景中的有效性。我们的代码可在 https://github.com/oucailab/AFENet 获取。 显示英文摘要

摘要： Semantic segmentation of high-resolution remote sensing images plays a crucial role in land-use monitoring and urban planning. Recent remarkable progress in deep learning-based methods makes it possible to generate satisfactory segmentation results. However, existing methods still face challenges in adapting network parameters to various land cover distributions and enhancing the interaction between spatial and frequency domain features. To address these challenges, we propose the Adaptive Frequency Enhancement Network (AFENet), which integrates two key components: the Adaptive Frequency and Spatial feature Interaction Module (AFSIM) and the Selective feature Fusion Module (SFM). AFSIM dynamically separates and modulates high- and low-frequency features according to the content of the input image. It adaptively generates two masks to separate high- and low-frequency components, therefore providing optimal details and contextual supplementary information for ground object feature representation. SFM selectively fuses global context and local detailed features to enhance the network's representation capability. Hence, the interactions between frequency and spatial features are further enhanced. Extensive experiments on three publicly available datasets demonstrate that the proposed AFENet outperforms state-of-the-art methods. In addition, we also validate the effectiveness of AFSIM and SFM in managing diverse land cover types and complex scenarios. Our codes are available at https://github.com/oucailab/AFENet.