We introduce a novel joint sparse representation based multi-view automatic target recognition (ATR) method, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views of the same physical target for a single joint recognition decision. Extensive experiments have been carried out on moving and stationary target acquisition and recognition (MSTAR) public database to evaluate the proposed method compared with several state-of-the-art methods such as linear support vector machine (SVM), kernel SVM, as well as a sparse representation based classifier (SRC). Experimental results demonstrate that the proposed joint sparse representation ATR method is very effective and performs robustly under variations such as multiple joint views, depression, azimuth angles, target articulations, as well as configurations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-View Automatic Target Recognition using Joint Sparse Representation


    Contributors:
    Haichao Zhang (author) / Nasrabadi, N. M. (author) / Zhang, Y. (author) / Huang, T. S. (author)


    Publication date :

    2012-07-01


    Size :

    5222374 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    High Range Resolution Profile Automatic Target Recognition Using Sparse Representation

    Nuo, Z. / Wei, C. | British Library Online Contents | 2010


    Multi-target Recognition for Hyperspectral Images Based on Sparse Representation and Multi-scale Analysis

    Li, Fangfang / Sun, Kang / Shicheng, Wang et al. | Springer Verlag | 2025



    Automatic View Recognition in Echocardiogram Videos Using Parts-Based Representation

    Ebadollahi, S. / Chang, S.-F. / Wu, H. et al. | British Library Conference Proceedings | 2004


    Small infrared target detection using sparse ring representation

    Chenqiang Gao / Tianqi Zhang / Qiang Li | IEEE | 2012