Robust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)
arXiv:1506.04870 · doi:10.1109/TGRS.2016.2580702
Abstract
The recently introduced collaborative nonnegative matrix factorization (CoNMF) algorithm was conceived to simultaneously estimate the number of endmembers, the mixing matrix, and the fractional abundances from hyperspectral linear mixtures. This paper introduces R-CoNMF, which is a robust version of CoNMF. The robustness has been added by a) including a volume regularizer which penalizes the distance to a mixing matrix inferred by a pure pixel algorithm; and by b) introducing a new proximal alternating optimization (PAO) algorithm for which convergence to a critical point is guaranteed. Our experimental results indicate that R-CoNMF provides effective estimates both when the number of endmembers are unknown and when they are known.
4 pages in IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, 2015
Cited by in corpus (6)
- Deep Hyperspectral Unmixing using Transformer Network
- Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms
- Unsupervised Pansharpening Based on Self-Attention Mechanism
- Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing
- Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
- Feature-specific correlation of structural, optical, and chemical properties in the transmission electron microscope with hypermodal data fusion