A Frequency Attention-Enhanced Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Semantic segmentation of high-resolution remote sensing images (HRRSIs) presents unique challenges due to the intricate spatial and spectral characteristics of these images. Traditional methods often prioritize spatial information while underutilizing the rich spectral context, leading to limited feature discrimination capabilities. To address these issues, we propose a novel frequency attention-enhanced network (FAENet), which incorporates a frequency attention model (FreqA) to jointly model spectral and spatial contexts. FreqA leverages discrete wavelet transformation (DWT) to decompose input images into distinct frequency components, followed by a two-stage attention mechanism comprising inner-component channel attention (ICCA) and cross-component channel attention (CCCA). These mechanisms enhance spectral representation, which is further refined through a self-attention (SA) module to capture long-range dependencies before transforming back into the spatial domain. FAENet’s encoder–decoder architecture facilitates multiscale feature refinement, enabling effective segmentation. Extensive experiments on the ISPRS Potsdam and LoveDA benchmarks demonstrate that FAENet outperforms state-of-the-art models, achieving superior segmentation accuracy. Ablation studies further validate the contributions of ICCA and CCCA. Moreover, efficiency comparisons confirm the superiority of the proposed FAENet over other models.