Convolutional Neural Networks for SAR Image Segmentation

David Malmgren-Hansen, Morten Nobel-Jørgensen

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Abstract

Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images.

In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides this contribution we also suggest a new way to do pixel wise annotation of SAR images that replaces a human expert manual segmentation process, which is both slow and troublesome. Our method for annotation relies on 3D CAD models of objects and scene, and converts these to labels for all pixels in a SAR image.

Our algorithms are evaluated on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset which was released by the Defence Advanced Research Projects Agency during the 1990s. The method is not restricted to the type of targets imaged in MSTAR but can easily be extended to any SAR data where prior information about scene geometries can be estimated.
Original languageEnglish
Title of host publicationProceedings of IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2015)
Number of pages6
PublisherIEEE
Publication date2015
Pages231 - 236
DOIs
Publication statusPublished - 2015
Event15th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2015) - Abu Dhabi, United Arab Emirates
Duration: 7 Dec 201510 Dec 2015
Conference number: 15
http://ece.adu.ac.ae/ISSPIT2015/index.html

Conference

Conference15th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2015)
Number15
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period07/12/201510/12/2015
Internet address

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