This research demonstrates the adverse implications of using non-robust statistical methods for detecting anomalies in hyperspectral image data, and proposes the use of multivariate outlier detection methods as an alternative detection strategy. Existing outlier detection methods are adapted for use in a hyperspectral image context, and their performance is compared to the benchmark RX detector and a cluster-based anomaly detector. Tests conducted using both simulated data and actual hyperspectral imagery indicate that multivariate outlier detection methods can achieve superior detection performance relative to current non-robust detection methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Finding Hyperspectral Anomalies Using Multivariate Outlier Detection


    Contributors:


    Publication date :

    2007-03-01


    Size :

    2296445 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Damage detection using outlier analysis

    Worden, K. | Online Contents | 2000


    Parallel algorithm of anomalies detection in hyperspectral image with projection pursuit

    Wei, W. / Huijie, Z. / Chao, D. | British Library Online Contents | 2009


    Finding "anomalies" in an arbitrary image

    Honda, T. / Nayar, S.K. | IEEE | 2001


    Finding "Anomalies" in an Arbitrary Image

    Honda, T. / Nayar, S. / IEEE | British Library Conference Proceedings | 2001


    ST TD outlier detection

    Sun, Dihua / Zhao, Hongzhuan / Yue, Hang et al. | IET | 2017

    Free access