Name
Title | ||
|---|---|---|
Fusion of local statistical parameters for buried underwater mine detection in sonar imaging |
Abstract | ||
|---|---|---|
Detection of buried underwater objects, and especially mines, is a current crucial strategic task. Images provided by sonar systems allowing to penetrate in the sea floor, such as the synthetic aperture sonars (SASs), are of great interest for the detection and classification of such objects. However, the signal-to-noise ratio is fairly low and advanced information processing is required for a correct and reliable detection of the echoes generated by the objects. The detection method proposed in this paper is based on a data-fusion architecture using the belief theory. The input data of this architecture are local statistical characteristics extracted from SAS data corresponding to the first-, second-, third-, and fourth-order statistical properties of the sonar images, respectively. The interest of these parameters is derived from a statistical model of the sonar data. Numerical criteria are also proposed to estimate the detection performances and to validate the method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1155/2008/876092 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
sonar imaging,local statistical parameter,input data,detection method,local statistical characteristic,reliable detection,sonar image,sas data,sonar system,sonar data,fourth-order statistical property,detection performance | Computer vision,Object detection,Synthetic aperture radar,Computer science,Feature extraction,Sonar,Sensor fusion,Statistical model,Artificial intelligence,Synthetic aperture sonar,Underwater | Journal |
Volume | Issue | ISSN |
2008, | 1 | 1687-6180 |
Citations | PageRank | References |
6 | 0.65 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frédéric Maussang | 1 | 15 | 2.37 |
| M. Rombaut | 2 | 76 | 6.90 |
| J. Chanussot | 3 | 306 | 18.20 |
| A. Hé/tet | 4 | 6 | 0.65 |
| M. Amate | 5 | 15 | 1.35 |