Frequency-Compensated Network for Daily Arctic Sea Ice Concentration Prediction
Journal:
arXiv
Published Date:
Apr 23, 2025
Abstract
Accurately forecasting sea ice concentration (SIC) in the Arctic is critical
to global ecosystem health and navigation safety. However, current methods
still is confronted with two challenges: 1) these methods rarely explore the
long-term feature dependencies in the frequency domain. 2) they can hardly
preserve the high-frequency details, and the changes in the marginal area of
the sea ice cannot be accurately captured. To this end, we present a
Frequency-Compensated Network (FCNet) for Arctic SIC prediction on a daily
basis. In particular, we design a dual-branch network, including branches for
frequency feature extraction and convolutional feature extraction. For
frequency feature extraction, we design an adaptive frequency filter block,
which integrates trainable layers with Fourier-based filters. By adding
frequency features, the FCNet can achieve refined prediction of edges and
details. For convolutional feature extraction, we propose a high-frequency
enhancement block to separate high and low-frequency information. Moreover,
high-frequency features are enhanced via channel-wise attention, and temporal
attention unit is employed for low-frequency feature extraction to capture
long-range sea ice changes. Extensive experiments are conducted on a
satellite-derived daily SIC dataset, and the results verify the effectiveness
of the proposed FCNet. Our codes and data will be made public available at:
https://github.com/oucailab/FCNet .
