Name
Title | ||
|---|---|---|
Tridiagonal Folmat Enhanced Multivariance Products Representation Based Hyperspectral Data Compression. |
Abstract | ||
|---|---|---|
Hyperspectral imaging features an important issue in remote sensing and applications. Requirement to collect high volumes of hyper spectral data in remote sensing algorithms poses a compression problem. To this end, many techniques or algorithms have been develop ed and continues to be improved in scientific literature. In this paper, we propose a recently developed lossy compression method which ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JSTARS.2018.2851368 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Hyperspectral imaging,Matrix decomposition,Image coding,Data compression,Principal component analysis | Tridiagonal matrix,Computer vision,Lossy compression,Matrix decomposition,Algorithm,Image quality,Hyperspectral imaging,Artificial intelligence,Data compression,Compressed sensing,Principal component analysis,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 9 | 1939-1404 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeynep Gundogar | 1 | 0 | 0.34 |
| Behcet Ugur Töreyin | 2 | 0 | 1.01 |
| Metin Demiralp | 3 | 33 | 5.48 |