Name
Title | ||
|---|---|---|
Ship Detection With The Fuzzy C-Mean Clustering Algorithm Using Fully Polarimetric Sar |
Abstract | ||
|---|---|---|
A fuzzy c-mean clustering algorithm to detect ships is proposed using fully polarimetric SAR data. The algorithm is unsupervised. It does not need the statistical decision and the performance is not data specific, as often arises with CFAR methods. A distance measure, based on a complex Wishart distribution, is applied using the fuzzy c-means clustering algorithm. The algorithm makes use the statistical properties of polarimetric data, and takes advantage of a clustering algorithm. It is thus expected that the algorithm could include fully polarimetric backscattering information for ship detection. Its effectiveness is demonstrated by applying it to detect the targets in a set of AIRSAR data. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IGARSS.2007.4423007 | IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET |
Keywords | Field | DocType |
ship detection, fuzzy c-means, polarimetric synthetic aperture radar, Wishart distance | CURE data clustering algorithm,Computer science,Remote sensing,FSA-Red Algorithm,Fuzzy set,Artificial intelligence,Cluster analysis,Computer vision,Canopy clustering algorithm,Pattern recognition,Correlation clustering,Fuzzy logic,Constant false alarm rate | Conference |
Volume | Issue | ISSN |
null | null | 2153-6996 |
Citations | PageRank | References |
2 | 0.40 | 3 |
Authors | ||
3 | ||