Paes, Rafael L.Pagamisse, Aylton [UNESP]2014-05-272014-05-272011-10-19Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6935 LNCS, p. 582-589.0302-97431611-3349http://hdl.handle.net/11449/72750We are investigating the combination of wavelets and decision trees to detect ships and other maritime surveillance targets from medium resolution SAR images. Wavelets have inherent advantages to extract image descriptors while decision trees are able to handle different data sources. In addition, our work aims to consider oceanic features such as ship wakes and ocean spills. In this incipient work, Haar and Cohen-Daubechies-Feauveau 9/7 wavelets obtain detailed descriptors from targets and ocean features and are inserted with other statistical parameters and wavelets into an oblique decision tree. © 2011 Springer-Verlag.582-589engdecision treesremote sensingSARtarget detectionwaveletsData sourceDescriptorsImage descriptorsMaritime surveillanceOblique decision treeOcean featureSAR dataSAR ImagesSea surfacesShip wakesStatistical parametersDecision treesInformation technologyPlant extractsRemote sensingShipsTrees (mathematics)Discrete wavelet transformsTrabalho apresentado em evento10.1007/978-3-642-24082-9_71Acesso aberto2-s2.0-800540739050304271846229471