|Appears in Collections:||Biological and Environmental Sciences Journal Articles|
|Peer Review Status:||Refereed|
|Title:||A ship detector applying Principal Component Analysis to the polarimetric Notch Filter|
|Citation:||Zhang T, Marino A, Xiong H & Yu W (2018) A ship detector applying Principal Component Analysis to the polarimetric Notch Filter. Remote Sensing, 10 (6), Art. No.: 948. https://doi.org/10.3390/rs10060948.|
|Abstract:||Ship detection using polarimetric synthetic aperture radar (PolSAR) data has attracted a lot of attention in recent years. Polarimetry can provide information regarding the scattering mechanisms of targets, which helps discriminate between ships and sea clutter. This enhancement is particularly valuable when we aim at detecting smaller vessels in rough sea states. This work exploits a ship detector called the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF), and it is aimed at improving its performance especially when less polarimetric images are available (e.g., dual-polarimetric data). The idea is to design a new polarimetric feature vector containing more features that are renowned to allow separation between ships and sea clutter. Then, a Principal Component Analysis (PCA) is further used to reduce the dimensionality of the new feature space. Experiments on four real Sentinel-1 datasets are carried out to demonstrate the validity of the proposed method and compare it against other ship detectors. Analyses of the experimental results show that the proposed algorithm can not only reduce the false alarms significantly, but also enhance the target-to-clutter ratio (TCR) so that it can more effectively detect weaker ships.|
|Rights:||This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).|
|ship detector applying Principal Component Analysis.pdf||Fulltext - Published Version||4.47 MB||Adobe PDF||View/Open|
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