Quality Metrics Evaluation of Hyperspectral Images

16 citations as recorded by crossref.

1. HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis D. Ayuba et al.
2. Fast Hyperspectral Image Denoising and Destriping Method Based on Graph Laplacian Regularization X. Su et al.
3. PCC-GLR: Pearson correlation coefficient-based graph Laplacian regularization for hyperspectral image denoising C. Liu et al.
4. Label-free, real-time classification of leukemia cells using snapshot-based hyperspectral imaging Q. Li et al.
5. Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data X. Shen & L. Cao
6. Prediction of Forest Structural Parameters Using Airborne Full-Waveform LiDAR and Hyperspectral Data in Subtropical Forests X. Shen et al.
7. A Novel High-Speed, Low-Power CNTFET-Based Inexact Full Adder Cell for Image Processing Application of Motion Detector Y. Mehrabani et al.
8. Multi-Scale Differentiated Network with Spatial–Spectral Co-Operative Attention for Hyperspectral Image Denoising X. Chang et al.
9. An efficient inexact Full Adder cell design in CNFET technology with high-PSNR for image processing R. Ataie et al.
10. Spectral super-resolution reconstruction of multispectral images based on low-rank coupled dictionary learning X. Lv et al.
11. Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation F. Yang et al.
12. DSCSRN: Physically Guided Symmetry-Aware Spatial-Spectral Collaborative Network for Single-Image Hyperspectral Super-Resolution X. Chang et al.
13. Spectral Super Resolution of Hyperspectral Images via Coupled Dictionary Learning K. Fotiadou et al.
14. Hyperspectral Image Denoising Based on Hyper-Laplacian Total Variation in Spectral Gradient Domain F. Yang et al.
15. SSCAN: A Spatial–Spectral Cross Attention Network for Hyperspectral Image Denoising Z. Wang et al.
16. Adversarial Spectral Super-Resolution for Multispectral Imagery Using Spatial Spectral Feature Attention Module Z. Liu et al.