Combining deep convolutional neural network and SVM to SAR image target recognition

Gao, Fei and Huang, Teng and Wang, Jun and Sun, Jinping and Yang, Erfu and Hussain, Amir; (2018) Combining deep convolutional neural network and SVM to SAR image target recognition. In: 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017. IEEE, GBR, pp. 1082-1085. ISBN 9781538630655 (https://doi.org/10.1109/iThings-GreenCom-CPSCom-Sm...)

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Abstract

To address the challenging problem on target recognition from synthetic aperture radar (SAR) images, a novel method is proposed by combining Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM). First, an improved DCNN is employed to learn the features of SAR images. Then, a SVM is utilized to map the leant features into the output labels. To enhance the feature extraction capability of DCNN, a class of separation information is also added to the cross-entropy cost function as a regularization term. As a result, this explicitly facilitates the intra-class compactness and separability in the process of feature learning. Numerical experiments are performed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. The results demonstrate that the proposed method can achieve an average accuracy of 99.15% on ten types of targets.

  • Item type:Book Section
    ID code:64414
    Dates:
    Date
    Event
    30 January 2018
    Published
    30 June 2017
    Published Online
    22 April 2017
    Accepted
    Notes:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology
    Department:Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:11 Jun 2018 15:23
    Last modified:08 Apr 2024 13:47
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/64414
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