Conventional remote sensing classification techniques that model the data in each class with a multivariate Gaussian distribution are inefficient, as this assumption is generally not valid in practice. We present a novel, independent component analysis (ICA) based approach for unsupervised classification of hyperspectral imagery. ICA, employed for a mixture model, estimates the data density in each class and models class distributions with nonGaussian structure, formulating the ICA mixture model (ICAMM). We apply the ICAMM for unsupervised classification of a test image from the AVIRIS sensor. Four feature extraction techniques namely principal component analysis, segmented principal component analysis, orthogonal subspace projection and projection pursuit have been considered as preprocessing steps for reducing the data dimensionality. The results demonstrate that the ICAMM significantly outperforms the K-means algorithm for land cover classification of hyperspectral imagery implemented on reduced data sets. Moreover, datasets extracted using segmented principal component analysis produce the highest classification accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    ICA mixture model based unsupervised classification of hyperspectral imagery


    Contributors:
    Shah, C.A. (author) / Arora, M.K. (author) / Robila, S.A. (author) / Varshney, P.K. (author)


    Publication date :

    2002-01-01


    Size :

    331608 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    ICA Mixture Model Based Unsupervised Classification of Hyperspectral Imagery

    Shah, C. / Arora, M. / Robila, S. et al. | British Library Conference Proceedings | 2002




    Feature extraction and classification for EO-1 hyperspectral imagery

    Castano, B. / Chien, S. / Cichy, B. et al. | NTRS | 2003


    Classification of Hyperspectral imagery Using SIFT for Spectral Matching

    Xu, Yiping / Hu, Kaoning / Tian, Yan et al. | IEEE | 2008