Name
Abstract | ||
|---|---|---|
In this paper, a novel robust graph-based adequate and concise information representation paradigm is explored. This new signal representation framework can provide a promising alternative for manifesting the essential structure of random signals. A typical application, namely, target detection within sea clutter, can thus be carried out using our proposed new graph-based signal characterization. ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TGRS.2019.2911451 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Clutter,Feature extraction,Object detection,Quantization (signal),Radar clutter,Detectors | Graph,Computer vision,Object detection,Monte Carlo method,Clutter,Feature extraction,Artificial intelligence,Detector,Mathematics,Information representation | Journal |
Volume | Issue | ISSN |
57 | 9 | 0196-2892 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Yan | 1 | 15 | 8.53 |
| Y. Bai | 2 | 62 | 13.59 |
| Hsiao-chun Wu | 3 | 959 | 97.99 |
| Xiangli Zhang | 4 | 4 | 3.52 |