We present results quantifying the exploitability of compressed remote sensing imagery. The performance of various feature extraction and classification tasks is measured on hyperspectral images coded using the JPEG-2000 Standard. Spectral decorrelation is performed using the Karhunen-Loeve transform and the 9-7 wavelet transform as part of the JPEG-2000 process. The quantitative performance of supervised, unsupervised, and hybrid classification tasks is reported as a function of the compressed bit rate for each spectral decorrelation scheme. The tasks examined are shown to perform with 99% accuracy at rates as low as 0.125 bits/pixel/band. This suggests that one need not limit remote sensing systems to lossless compression only, since many common classification tools perform reliably on images compressed to very low bit rates.


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    Titel :

    Feature extraction from hyperspectral images compressed using the JPEG-2000 standard


    Beteiligte:
    Pal, M.D. (Autor:in) / Brislawn, C.M. (Autor:in) / Brumby, S.R. (Autor:in)


    Erscheinungsdatum :

    2002-01-01


    Format / Umfang :

    527144 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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