Pose-informed deep learning method for SAR ATR

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dc.contributor.authorBelloni, Carole
dc.contributor.authorAouf, Nabil
dc.contributor.authorBalleri, Alessio
dc.contributor.authorLe Caillec, Jean-Marc
dc.contributor.authorMerlet, Thomas
dc.date.accessioned2020-09-14T15:34:30Z
dc.date.available2020-09-14T15:34:30Z
dc.date.issued2020-03-30
dc.description.abstractSynthetic aperture radar (SAR) images for automatic target classification (automatic target recognition (ATR)) have attracted significant interest as they can be acquired day and night under a wide range of weather conditions. However, SAR images can be time consuming to analyse, even for experts. ATR can alleviate this burden and deep learning is an attractive solution. A new deep learning Pose-informed architecture solution, that takes into account the impact of target orientation on the SAR image as the scatterers configuration changes, is proposed. The classification is achieved in two stages. First, the orientation of the target is determined using a Hough transform and a convolutional neural network (CNN). Then, classification is achieved with a CNN specifically trained on targets with similar orientations to the target under test. The networks are trained with translation and SAR-specific data augmentation. The proposed Pose-informed deep network architecture was successfully tested on the Military Ground Target Dataset (MGTD) and the Moving and Stationary Target Acquisition and Recognition (MSTAR) datasets. Results show the proposed solution outperformed standard AlexNets on the MGTD, MSTAR extended operating condition (EOC)1, EOC2 and standard operating condition (SOC)10 datasets with a score of 99.13% on the MSTAR SOC10.en_UK
dc.identifier.citationBelloni C, Aouf N, Balleri A, et al., (2020) Pose-informed deep learning method for SAR ATR. IET Radar Sonar and Navigation, Volume 14, Issue 11, November 2020, pp. 1649-1658en_UK
dc.identifier.issn1751-8784
dc.identifier.urihttps://doi.org/10.1049/iet-rsn.2019.0615
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/15799
dc.language.isoenen_UK
dc.publisherThe institution of Engineering and Technology (IET)en_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectradar target recognitionen_UK
dc.subjectradar imagingen_UK
dc.subjectlearning (artificial intelligence)en_UK
dc.subjectsynthetic aperture radaren_UK
dc.subjectimage recognitionen_UK
dc.subjectHough transformsen_UK
dc.subjectneural netsen_UK
dc.subjectimage classificationen_UK
dc.titlePose-informed deep learning method for SAR ATRen_UK
dc.typeArticleen_UK

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