Name
Title | ||
|---|---|---|
Using distributed compressed sensing to derive continuous hyperspectral imaging from a wireless sensor network. |
Abstract | ||
|---|---|---|
•Hyperspectral data can be acquired from a network of multispectral sensors.•Equipping multispectral sensors with random filters enables such a network.•DCS delivers the best hyperspectral data for most datasets.•For small networks in homogenous areas, UPDM delivers superior results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compag.2019.104974 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
Hyperspectral imaging,Compressed sensing,Wireless sensor network | Computer vision,Satellite,Multispectral image,Real-time computing,Hyperspectral imaging,Spectral resolution,Continuous monitoring,Artificial intelligence,Engineering,Image resolution,Wireless sensor network,Compressed sensing | Journal |
Volume | ISSN | Citations |
166 | 0168-1699 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Hänel | 1 | 0 | 0.34 |
| Thomas Jarmer | 2 | 0 | 0.34 |
| Nils Aschenbruck | 3 | 555 | 56.28 |