5 papers · 1 filter
Dual-Branch State-Displacement Network for Sea Surface Temperature Super-Resolution
Wankun Chen, Feng Gao, Yanhai Gan +4
Sea surface temperature (SST) is a critical indicator of global climate change, yet satellite-derived SST imagery often suffers from coarse spatial resolution, limiting the ability…
Frequency-Enhanced Hilbert Scanning Mamba for Short-Term Arctic Sea Ice Concentration Prediction
Feng Gao, Zheng Gong, Wenli Liu +4
While Mamba models offer efficient sequence modeling, vanilla versions struggle with temporal correlations and boundary details in Arctic sea ice concentration (SIC) prediction. To…
Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution
Wankun Chen, Feng Gao, Yanhai Gan +3
Sea surface temperature (SST) is an essential indicator of global climate change and one of the most intuitive factors reflecting ocean conditions. Obtaining high-resolution SST da…
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism
Zhaomin Fang, Wankun Chen, Feng Gao +3
Arctic Sea Ice Concentration (SIC) is the ratio of ice-covered area to the total sea area of the Arctic Ocean, which is a key indicator for maritime activities. Nowadays, we often…
LSKSANet: A Novel Architecture for Remote Sensing Image Semantic Segmentation Leveraging Large Selective Kernel and Sparse Attention Mechanism
Miao Fu, Feng Gao, Ruzhuang Hua +3
In this paper, we proposed large selective kernel and sparse attention network (LSKSANet) for remote sensing image semantic segmentation. The LSKSANet is a lightweight network that…