Hyperspectral Unmixing from Incomplete and Noisy Data
AbstractIn hyperspectral images, once the pure spectra of the materials are known, hyperspectral unmixing seeks to find their relative abundances throughout the scene. We present a novel variational model for hyperspectral unmixing from incomplete noisy data, which combines a spatial regularity prior with the knowledge of the pure spectra. The material abundances are found by minimizing the resulting convex functional with a primal dual algorithm. This extends least squares unmixing to the case of incomplete data, by using total variation regularization and masking of unknown data. Numerical tests with artificial and real-world data demonstrate that our method successfully recovers the true mixture coefficients from heavily-corrupted data. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Montag, M.J.; Stephani, H. Hyperspectral Unmixing from Incomplete and Noisy Data. J. Imaging 2016, 2, 7.
Montag MJ, Stephani H. Hyperspectral Unmixing from Incomplete and Noisy Data. Journal of Imaging. 2016; 2(1):7.Chicago/Turabian Style
Montag, Martin J.; Stephani, Henrike. 2016. "Hyperspectral Unmixing from Incomplete and Noisy Data." J. Imaging 2, no. 1: 7.