A ship detector applying principal component analysis to the Polarimetric Notch Filter

Citation

Zhang T, Marino A & Xiong H (2017) A ship detector applying principal component analysis to the Polarimetric Notch Filter. In: volume 2017-July. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Texas, USA, 23.07.2017-28.07.2017. Institute of Electrical and Electronics Engineers, pp. 1864-1867. https://doi.org/10.1109/IGARSS.2017.8127340

Abstract
In this paper, a new algorithm for ship detection with Synthetic Aperture Radar (SAR) images is presented. We develop the proposed method by combing Principal Component Analysis (PCA) and the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF) method. In the first step, we replace the feature vector composed by the elements of the covariance matrix with more polarimetric features. Then, PCA is used to reduce the feature space. The new reduced feature vector is then used to detect ships by using the framework of the GP-PNF. In order to demonstrate the effectiveness of the proposed method, we exploited Sentinel-1 datasets. In this abstract, a dataset obtained in Gibraltar is considered. A comparison with other methods showed improvements in detection capability.

Journal
International Geoscience and Remote Sensing Symposium (IGARSS): Volume 2017-July

People

Dr Armando Marino

Associate Professor, Biological and Environmental Sciences