1 citations · 1 across the 5 of their papers we have counts for
5 papers
WGDnet: Wishart-guided Geometric-aware Deep Network for PolSAR Image Classification
Junfei Shi, Haojia Zhang, Yu Cheng +1
Polarimetric Synthetic Aperture Radar (PolSAR) classification underpins all-weather Earth observation. Conventional Wishart methods depend on rigid handcrafted operators with limit…
Direction-adaptive Mamba: Spatial-Frequency Dual-Domain Collaborative Learning for PolSAR Image Classification
Junfei Shi, Yu Cheng, Haojia Zhang +3
Deep learning dominates polarimetric synthetic aperture radar (PolSAR) image classification, with Mamba architectures serving as favorable backbones due to linear complexity and st…
Generative AI for subgrid turbulence in large-eddy simulations
Yu Cheng, Tianle Liu
Turbulence governs the transport of momentum, energy, and scalars in many geophysical and engineering flows. In large-eddy simulations (LES), parameterizing subgrid-scale (SGS) str…
Knowledge-guided Complex Diffusion Model for PolSAR Image Classification in Contourlet Domain
Junfei Shi, Yu Cheng, Haiyan Jin +4
Diffusion models have demonstrated exceptional performance across various domains due to their ability to model and generate complicated data distributions. However, when applied t…
A Res-FCN for Electromagnetic Inversion of High Contrast Scatterers at an Arbitrary Frequency Within a Wide Frequency Band
Hao-Jie Hu, Jiawen Li, Li-Ye Xiao +2
Many successful machine learning methods have been developed for microwave inversion problems. However, so far, their inversion has been performed only at the specifically trained…