A Frequency Decoupling Network for Semantic Segmentation of Remote Sensing Images

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…

• NN
  National Natural Science Foundation of China

  Awards: 62401196, 42101458, 42130112
• NK
  National Key Research and Development Program of China

  Award: 2023YFC3209201
• FR
  Fundamental Research Funds for the Central Universities

  Award: B230201007