Name
Title | ||
|---|---|---|
A constrained formulation for compressive spectral image reconstruction using linear mixture models |
Abstract | ||
|---|---|---|
Recent hyperspectral imaging systems are constructed on the idea of compressive sensing for efficient acquisition. However, the traditional reconstruction model in compressive hyperspectral imaging has a high computational complexity. In this work, compressive hyperspectral imaging and unmixing are combined for hyperspectral reconstruction in a low-complexity scheme. The compressed hyperspectral measurements are acquired with a single pixel spectrometer. The reconstruction model is represented in a space of lower dimension named linear mixture model. Hyperspectral reconstruction is then formulated as a nonnegative matrix factorization problem with respect to the endmembers and abundances, bypassing high-complexity tasks involving the hyperspectral data cube itself. The nonnegative matrix factorization problem is solved by combining an alternating least-squares based estimation strategy with the alternating direction method of multipliers. The estimated performance of the proposed scheme is illustrated in experiments conducted on a simulated acquisition in real data outperforming in 3dB the state-of-the-art reconstruction algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CAMSAP.2017.8313122 | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
Keywords | Field | DocType |
constrained formulation,compressive spectral image reconstruction,linear mixture model,compressive sensing,compressive hyperspectral imaging,hyperspectral reconstruction,low-complexity scheme,compressed hyperspectral measurements,nonnegative matrix factorization problem,hyperspectral data cube,hyperspectral imaging systems,alternating least-squares based estimation strategy | Iterative reconstruction,Computer science,Algorithm,Hyperspectral imaging,Non-negative matrix factorization,Pixel,Mixture model,Compressed sensing,Data cube,Computational complexity theory | Conference |
ISBN | Citations | PageRank |
978-1-5386-1252-1 | 0 | 0.34 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jorge Bacca | 1 | 6 | 5.25 |
| Héctor Vargas | 2 | 48 | 6.63 |
| Henry Arguello | 3 | 90 | 30.83 |