Sparse unmixing via variable splitting and augmented Lagrangian for vegetation and urban area classification using Landsat data

7 citations as recorded by crossref.

1. Theoretical Principles and Perspectives of Hyperspectral Imaging Applied to Sediment Core Analysis K. Jacq et al. https://doi.org/10.3390/quat5020028
2. Autoencoder-Based Hyperspectral Unmixing with Simultaneous Number-of-Endmembers Estimation A. Alshahrani et al. https://doi.org/10.3390/s25082592
3. The paleoclimatic environment reconstruction of Lop Nur in NW China in UAV spectroscopy L. Yang et al. https://doi.org/10.1007/s00343-022-1341-9
4. Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing U. Kumar et al. https://doi.org/10.3390/rs9111105
5. Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used? K. Ren et al. https://doi.org/10.3390/rs12050882
6. A Locally Optimized Model for Hyperspectral and Multispectral Images Fusion K. Ren et al. https://doi.org/10.1109/TGRS.2021.3133670
7. HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model D. Wang et al. https://doi.org/10.1109/TPAMI.2025.3557581