Pose-informed deep learning method for SAR ATR

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dc.contributor.author Belloni, Carole
dc.contributor.author Aouf, Nabil
dc.contributor.author Balleri, Alessio
dc.contributor.author Le Caillec, Jean-Marc
dc.contributor.author Merlet, Thomas
dc.date.accessioned 2020-09-14T15:34:30Z
dc.date.available 2020-09-14T15:34:30Z
dc.date.issued 2020-03-30
dc.identifier.citation Belloni C, Aouf N, Balleri A, et al., (2020) Pose-informed deep learning method for SAR ATR. IET Radar Sonar and Navigation, Available online 30 March 2020, pp. 1-9 en_UK
dc.identifier.issn 1751-8784
dc.identifier.uri https://doi.org/10.1049/iet-rsn.2019.0615
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/15799
dc.description.abstract Synthetic 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.language.iso en en_UK
dc.publisher The institution of Engineering and Technology (IET) en_UK
dc.rights Attribution-NonCommercial 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/ *
dc.subject radar target recognition en_UK
dc.subject radar imaging en_UK
dc.subject learning (artificial intelligence) en_UK
dc.subject synthetic aperture radar en_UK
dc.subject image recognition en_UK
dc.subject Hough transforms en_UK
dc.subject neural nets en_UK
dc.subject image classification en_UK
dc.title Pose-informed deep learning method for SAR ATR en_UK
dc.type Article en_UK


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