Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation

• URL: http://arxiv.org/abs/2504.02647v1
• Date: Thu, 03 Apr 2025 14:42:49 GMT
• Title: Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation
• Authors: Feng Gao, Miao Fu, Jingchao Cao, Junyu Dong, Qian Du,
• Abstract summary: 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)<n>AFSIM dynamically separates and modulates high- and low-frequency features according to the content of the input image.<n>SFM selectively fuses global context and local detailed features to enhance the network's representation capability.
• Score: 33.49405456617909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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