Name
Abstract | ||
|---|---|---|
The goal of hyperspectral unmixing is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels into N constituent material spectra (or “end-members”) with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing based on loopy belief propagation (BP) that enables the exploitation of spectral coherence in the end-memb... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCI.2015.2465161 | IEEE Transactions on Computational Imaging |
Keywords | Field | DocType |
Approximation methods,Manganese,Hyperspectral imaging,Spatial coherence,Message passing,Coherence,Computational modeling | Factor graph,Discrete mathematics,Coherence (signal processing),Matrix decomposition,Algorithm,Theoretical computer science,Hyperspectral imaging,Spectral bands,Mathematics,Message passing,Belief propagation,Bilinear interpolation | Journal |
Volume | Issue | ISSN |
1 | 3 | 2573-0436 |
Citations | PageRank | References |
3 | 0.38 | 42 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremy P. Vila | 1 | 106 | 4.38 |
| Philip Schniter | 2 | 1620 | 93.74 |
| Joseph Meola | 3 | 3 | 0.38 |