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Ship detection in SAR images based on Maxtree representation and graph signal processing

Author
Salembier, P.; Liesegang, S.; Lopez, C.
Type of activity
Journal article
Journal
IEEE transactions on geoscience and remote sensing
Date of publication
2019-05
Volume
57
Number
5
First page
2709
Last page
2724
DOI
https://doi.org/10.1109/TGRS.2018.2876603 Open in new window
Project funding
Heterogeneous information and graph signal processing for the Big Data era. Application to high-throughput, remote sensing, multimedia and human computer interfaces
Multimodal Signal Processing and Machine Learning on Graphs
Repository
http://hdl.handle.net/2117/125211 Open in new window
URL
https://ieeexplore.ieee.org/document/8529215 Open in new window
Abstract
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 exi...
Citation
Salembier, P., Liesegang, S., Lopez, C. Ship detection in SAR images based on Maxtree representation and graph signal processing. "IEEE transactions on geoscience and remote sensing", 1 Gener 2018.
Keywords
Branch filter, Graph filter, Graph signal processing, Machine learning, Maxtree, Ship detection, Support vector machine (SVM), Synthetic-aperture radar (SAR), Tree filter
Group of research
CommSensLab-UPC - Centre Específic de Recerca en Comunicació i Detecció UPC
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
RSLAB - Remote Sensing Lab
SPCOM - Signal Processing and Communications Group

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