HSI Fusion Method Based on TV-CNMF and SCT-NMF Under the Background of Artificial Intelligence

The fusion of hyper-spectral images has important application value in fields such as remote sensing, environmental monitoring, and agricultural analysis. To improve the quality of reconstructed images, an HSI fusion method based on fully variational coupled non-negative matrix factorization and sparse constrained tensor factorization techniques is proposed. Spectral sparsity description is enhanced through sparse regularization, image spatial characteristics are captured using differential operators, and convergence is improved by combining proximal optimization with augmented Lagrangian methods. The experiment outcomes on the AVIRIS and HYDICE datasets indicate that the proposed method achieves peak signal-to-noise ratios of 38.12 dB and 37.56 dB, respectively, and reduces spectral angle errors to 3.98° and 4.12°, respectively, significantly better than the other two comparative methods. The contribution of each module is further verified through ablation experiments. The complete algorithm performs the best in all indicators, verifying the synergistic effect of sparse regularization, total variation regularization, and coupled factorization strategies. In HSI fusion tasks under various complex lighting and noise conditions, the performance of the proposed algorithm is particularly excellent, fully demonstrating its robustness and applicability in complex scenes. The method proposed by the research effectively improves the fusion quality of HSI, providing an efficient and robust solution for the analysis and application of HSI.

@article{Zhao2025,
  title     = {HSI Fusion Method Based on TV-CNMF and SCT-NMF Under the Background of Artificial Intelligence},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Dapeng Zhao and Yapeng Zhao and Xuexia Dou},
  doi       = {10.14569/IJACSA.2025.0160437},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160437}
}