Name
Abstract | ||
|---|---|---|
This paper presents an automatic ship detection algorithm for polarimetric synthetic aperture radar (PolSAR) data. Based on the non-Gaussian K-Wishart distribution model for complex backscattering coefficients, the PolSAR image is clustered automatically by a modified expectation maximization algorithm. A goodness-of-fit test is incorporated to improve the model fitness of the cluster iteratively. Then, the SPAN of ship cluster center is used to detect ships. Finally, the experimental results of a real measured UAVSAR dataset show that the proposed algorithm could improve the ability of weak target detection while reduces the rate of false alarm and miss detections. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7730236 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
Goodness-of-fit test, K-Wishart classifier, polarimetric synthetic aperture radar (PolSAR), ship detection | Data modeling,False alarm,Pattern recognition,Computer science,Expectation–maximization algorithm,Backscatter,Remote sensing,Artificial intelligence,Cluster analysis,Wishart distribution,Detector,Probability density function | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Fan | 1 | 53 | 7.96 |
| Zhou, F. | 2 | 25 | 2.98 |
| Mingliang Tao | 3 | 68 | 10.49 |
| Xueru Bai | 4 | 169 | 25.80 |