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urn:hdl:2117/125211 Ship detection in SAR images based on Maxtree representation and graph signal processing Salembier Clairon, Philippe Jean Liesegang Maria, Sergi López Martínez, Carlos Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció Earth sciences Remote sensing Branch filter Graph filter Graph signal processing Machine learning Maxtree Ship detection Support vector machine (SVM) Synthetic-aperture radar (SAR) Tree filter Ciències de la terra Teledetecció © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper discusses an image processing architecture and tools to address the problem of ship detection in synthetic-aperture radar images. The detection strategy relies on a tree-based representation of images, here a Maxtree, and graph signal processing tools. Radiometric as well as geometric attributes are evaluated and associated with the Maxtree nodes. They form graph attribute signals which are processed with graph filters. The goal of this filtering step is to exploit the correlation existing between attribute values on neighboring tree nodes. Considering that trees are specific graphs where the connectivity toward ancestors and descendants may have a different meaning, we analyze several linear, nonlinear, and morphological filtering strategies. Beside graph filters, two new filtering notions emerge from this analysis: tree and branch filters. Finally, we discuss a ship detection architecture that involves graph signal filters and machine learning tools. This architecture demonstrates the interest of applying graph signal processing tools on the tree-based representation of images and of going beyond classical graph filters. The resulting approach significantly outperforms state-of-the-art algorithms. Finally, a MATLAB toolbox allowing users to experiment with the tools discussed in this paper on Maxtree or Mintree has been created and made public. Peer Reviewed Postprint (author's final draft) 2018-01-01 Article https://ieeexplore.ieee.org/document/8529215 info:eu-repo/grantAgreement/MINECO//TEC2013-43935-R/ES/PROCESADO DE INFORMACION HETEROGENEA Y SEÑALES EN GRAFOS PARA BIG DATA. APLICACION EN CRIBADO DE ALTO RENDIMIENTO, TELEDETECCION, MULTIMEDIA Y HCI./ info:eu-repo/grantAgreement/MINECO/1PE/TEC2016-75976-R Open Access Institute of Electrical and Electronics Engineers (IEEE)