High-Order Neural Network-Based Ship Detection Algorithms Applied to SAR Imagery
Conference: EUSAR 2012 - 9th European Conference on Synthetic Aperture Radar
04/23/2012 - 04/26/2012 at Nuremberg, Germany
Proceedings: EUSAR 2012
Pages: 4Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
de Nicolás-Presa, Jaime Martín; de la Mata-Moya, David; Jarabo-Amores, Maria-Pilar; Bárcena-Humanes, Jose-Luis; Palma-Vazquez, Angel (Signal Theory and Communications Department, Higher Polytechnic School, University of Alcala, 28805 Alcalá de Henares, Madrid, Spain)
Ship detection is traditionally carried out with patrol ships or aircrafts, with a limited coverage area and also limited by weather conditions. Synthetic aperture radars can surpass these limitations. In this paper, two neural network-based techniques are proposed and compared for detecting ships over a TerraSAR-X image. The first technique is based on a second-order neural network whereas the second one is based on the combination of a Zernike moments-based feature extractor and a Multilayer Perceptron. Good results are obtained with both techniques but the Zernike moments-based one gives rise to the best detection results with less processing time.