In this paper, a tutorial overview on anomaly detection for hyperspectral electro-optical systems is presented. This tutorial is focused on those techniques that aim to detect small man-made anomalies typically found in defense and surveillance applications. Since a variety of methods have been proposed for detecting such targets, this tutorial places emphasis on the techniques that are either mathematically more tractable or easier to interpret physically. These methods are not only more suitable for a tutorial publication, but also an essential to a study of anomaly detection. Previous surveys on this subject have focused mainly on anomaly detectors developed in a statistical framework and have been based on well-known background statistical models. However, the most recent research trends seem to move away from the statistical framework and to focus more on deterministic and geometric concepts. This work also takes into consideration these latest trends, providing a wide theoretical review without disregarding practical recommendations about algorithm implementation. The main open research topics are addressed as well, the foremost being algorithm optimization, which is required for embodying anomaly detectors in real-time systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A tutorial overview of anomaly detection in hyperspectral images


    Contributors:
    Matteoli, S (author) / Diani, M (author) / Corsini, G (author)


    Publication date :

    2010-07-01


    Size :

    2225950 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Method for Object-Based Anomaly Detection in Hyperspectral Images

    de Agirre, A.M. / Rodriguez-Cuenca, B. / Alonso, M.C. et al. | British Library Conference Proceedings | 2013


    REMIX: Real-Time Hyperspectral Anomaly Detection for Small UAVs

    Dastranj, Melika / de Smet, Timothy / Wigdahl-Perry, Courtney et al. | IEEE | 2025


    Topological Learning for Semi-Supervised Anomaly Detection in Hyperspectral Imagery

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


    Applied low dimension linear manifold in hyperspectral imagery anomaly detection

    Li, Zhiyong / Wang, Liangliang / Zheng, Siyuan | SPIE | 2014