Support subspaces method for synthetic aperture radar automatic target recognition

This article offers a new object recognition approach that gives high quality using synthetic aperture radar images. The approach includes image preprocessing, clustering and recognition stages. At the image preprocessing stage, we compute the mass centre of object images for better image matching....

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Main Authors: Vladimir Fursov, Denis Zherdev, Nikolay Kazanskiy
Format: Article
Language:English
Published: SAGE Publishing 2016-09-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.1177/1729881416664848
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author Vladimir Fursov
Denis Zherdev
Nikolay Kazanskiy
author_facet Vladimir Fursov
Denis Zherdev
Nikolay Kazanskiy
author_sort Vladimir Fursov
collection DOAJ
description This article offers a new object recognition approach that gives high quality using synthetic aperture radar images. The approach includes image preprocessing, clustering and recognition stages. At the image preprocessing stage, we compute the mass centre of object images for better image matching. A conjugation index of a recognition vector is used as a distance function at clustering and recognition stages. We suggest a construction of the so-called support subspaces, which provide high recognition quality with a significant dimension reduction. The results of the experiments demonstrate that the proposed method provides higher recognition quality (97.8%) than such methods as support vector machine (95.9%), deep learning based on multilayer auto-encoder (96.6%) and adaptive boosting (96.1%). The proposed method is stable for objects processed from different angles.
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spelling doaj.art-bec5478362164fb3a40516c4b5dda1242022-12-22T01:07:10ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142016-09-011310.1177/172988141666484810.1177_1729881416664848Support subspaces method for synthetic aperture radar automatic target recognitionVladimir FursovDenis ZherdevNikolay KazanskiyThis article offers a new object recognition approach that gives high quality using synthetic aperture radar images. The approach includes image preprocessing, clustering and recognition stages. At the image preprocessing stage, we compute the mass centre of object images for better image matching. A conjugation index of a recognition vector is used as a distance function at clustering and recognition stages. We suggest a construction of the so-called support subspaces, which provide high recognition quality with a significant dimension reduction. The results of the experiments demonstrate that the proposed method provides higher recognition quality (97.8%) than such methods as support vector machine (95.9%), deep learning based on multilayer auto-encoder (96.6%) and adaptive boosting (96.1%). The proposed method is stable for objects processed from different angles.https://doi.org/10.1177/1729881416664848
spellingShingle Vladimir Fursov
Denis Zherdev
Nikolay Kazanskiy
Support subspaces method for synthetic aperture radar automatic target recognition
International Journal of Advanced Robotic Systems
title Support subspaces method for synthetic aperture radar automatic target recognition
title_full Support subspaces method for synthetic aperture radar automatic target recognition
title_fullStr Support subspaces method for synthetic aperture radar automatic target recognition
title_full_unstemmed Support subspaces method for synthetic aperture radar automatic target recognition
title_short Support subspaces method for synthetic aperture radar automatic target recognition
title_sort support subspaces method for synthetic aperture radar automatic target recognition
url https://doi.org/10.1177/1729881416664848
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AT deniszherdev supportsubspacesmethodforsyntheticapertureradarautomatictargetrecognition
AT nikolaykazanskiy supportsubspacesmethodforsyntheticapertureradarautomatictargetrecognition