Name
Title | ||
|---|---|---|
Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing |
Abstract | ||
|---|---|---|
Hyperspectral imaging is an active research area in remote sensing. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. In this paper, we introduce a new compressed sensing methodology, termed Hyperspectral coded aperture (HYCA), which exploits the high correlation existing among the components of remotely sensed hyperspectral data sets to reduce the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images: the spectral vectors live on a low dimensional subspace and the spectral bands are piecewise smooth. The former property allows to represent the data vectors using a small number of coordinates, and the latter implies that each coordinate is piecewise smooth and thus compressible on local differences. The reconstruction of the data cube is obtained by minimizing a convex objective function containing a data term associated to the compressed measurements and a total variation spatial regularizer. A series of experiments with simulated and real data show the effectiveness of the newly developed HYCA, indicating that the proposed scheme has a high potential in real-world applications. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6351279 | Signal Processing Conference |
Keywords | Field | DocType |
hyperspectral imaging,remote sensing,image representation,image coding,image fusion,compressive sensing,spectral unmixing,data compression,lossy compression framework,hyperspectral coded aperture,high dimensional synthetic hyperspectral data,total variation,image reconstruction,spectral vector,hyperspectral compressive sensing,hyca,compressed sensing,optimization,original data cube reconstruction,geophysical image processing,hyperspectral image data,signal subspace,piecewise smooth spectral band,spatial correlation,correlation methods,vectors,data term association,image compression strategy,coded aperture | Computer vision,Data set,Full spectral imaging,Coded aperture,Computer science,Hyperspectral imaging,Artificial intelligence,Signal subspace,Piecewise,Compressed sensing,Data cube | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4673-1158-8 | 978-1-4673-1158-8 | 2 |
PageRank | References | Authors |
0.38 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Martin | 1 | 69 | 5.35 |
| José M. Bioucas-Dias | 2 | 3565 | 173.67 |
| Antonio Plaza | 3 | 3475 | 262.63 |