In this paper, a new approach of anomaly detection based on low dimensional manifold will be elaborated. Hyperspectral image data set is considered as a low-dimensional manifold embedded in the high-dimensional spectral space, and this manifold has special geometrical structure, such as Hyper-plane. Usually, the main body of this manifold is constituted by a large area of background spectrum while the anomalistic objects are outside of the manifold. Through the analysis of the geometrical characteristics and the calculation of the appropriate projection direction, anomalistic objects can be separated from background effectively, so as to achieve the purpose of anomaly detection. Experimental results obtained from both the ground and airborne spectrometer data prove effectiveness of the algorithm in improving the detection performance. Since there are no available prior target spectrums to provide proper projected direction, the weak anomalies which have subtle differences from the background on the spectrum will be undetected.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Applied low dimension linear manifold in hyperspectral imagery anomaly detection


    Beteiligte:
    Li, Zhiyong (Autor:in) / Wang, Liangliang (Autor:in) / Zheng, Siyuan (Autor:in)

    Kongress:

    Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013 ; 2013 ; SuZhou,China


    Erschienen in:

    Erscheinungsdatum :

    21.02.2014





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Applied low dimension linear manifold in hyperspectral imagery anomaly detection [9142-76]

    Li, Z. / Wang, L. / Zheng, S. et al. | British Library Conference Proceedings | 2014


    Topological Learning for Semi-Supervised Anomaly Detection in Hyperspectral Imagery

    Ramirez, Juan / Armitage, Tristan / Bihl, Trevor et al. | IEEE | 2019


    Real-time causal processing of anomaly detection for hyperspectral imagery

    Shih-Yu Chen / Yulei Wang / Chao-Cheng Wu et al. | IEEE | 2014



    6.0105 Using Self-Organizing Maps for Anomaly Detection in Hyperspectral Imagery

    Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2002