most citedHyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer

28 citations · 31 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026★ 1 cited

Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks

Zekun Long, Judy X. Yang, Jing Wang +3

Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling between material properties and…

cs.CV2026

HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone

Guanyiman Fu, Jingtao Li, Zihang Cheng +8

While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification, ground-based hyperspectral pr…

cs.CV2026

WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusion

Zekun Long, Ali Zia, Guanyiman Fu +2

Weak spectral responses in hyperspectral images are often obscured by dominant endmembers and sensor noise, resulting in inaccurate abundance estimation. This paper introduces WS-N…

eess.IV2024★ 2 cited

SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising

Guanyiman Fu, Fengchao Xiong, Jianfeng Lu +1

Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due to noise arising from intra-imaging mechanisms and environmental factors. Long-range spatial-spectral…

eess.IV2024★ 28 cited

Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer

Guanyiman Fu, Fengchao Xiong, Jianfeng Lu +3

Hyperspectral images (HSIs) often suffer from noise arising from both intra-imaging mechanisms and environmental factors. Leveraging domain knowledge specific to HSIs, such as glob…