CLFDA: Continuous Low-Frequency Decomposition Architecture for Fine-Grained Land Cover Classification

Fine-grained land cover classification from high-resolution remote sensing imagery plays a vital role in urban and environmental monitoring. While existing spatial-domain based approaches achieve notable progress, their performance in complex scenarios remains constrained by insufficient modeling of characteristics. This letter proposes the continuous low-frequency decomposition architecture (CLFDA) to address insufficient cross-domain modeling of multi-scale frequency characteristics in current methods. The architecture introduces frequency domain features through continuous low-frequency decomposition, where each frequency decomposition and enhancement module employ discrete wavelet transform to separate spatial and low-frequency features into low-frequency and high-frequency subbands. Low-frequency features feed back into the encoder for global context, while high-frequency features are routed to the decoder via attention mechanisms for detail refinement, enabling bidirectional spatial-frequency fusion. By integrating convolutional neural networks, vision transformer, and mamba backbones, our CLFDA achieves 2.0% and 3.46% average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mIoU</i> improvements on the GID-15 and the FUSU datasets, respectively. These consistent performance gains across heterogeneous backbones demonstrate the effectiveness and generalizability of our CLFDA in modeling frequency domain features. The code is at https://github.com/GeoRSAI/CLFDA.