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Semantic segmentation of remote sensing images (RSIs) is vital for numerous geospatial applications, including land-use mapping, urban planning, and environmental monitoring. Traditional neural networks for semantic segmentation primarily focus on learning in the spatial domain, which often results in suboptimal performance due to the complexity of RSIs that exhibit diverse and intricate structures. To address this problem, we propose a novel frequency decoupling network (FDNet) that enhances feature representation by independently refining high-frequency and low-frequency components in the frequency domain. FDNet introduces three core components: a sparse-aware spectral enhancement module (SSEM) that optimizes spectral feature learning by compressing redundant information while highlighting informative spectral bands, a frequency decoupling attention module (FDAM) that precisely distinguishes and enhances high-frequency and low-frequency features and an attentive frequency context module (AFCM) that integrates SSEM and FDAM into a cohesive framework for enriched spectral context modeling. Extensive experiments conducted on four benchmark datasets demonstrate that FDNet outperforms several state-of-the-art methods, achieving superior segmentation accuracy and robustness across various terrains and imaging conditions. Ablation experiments further confirm the impacts of SSEM, FDAM, and AFCM.
Li et al. (Wed,) studied this question.