Change detection by the MAD method in hyperspectral image data

Allan Aasbjerg Nielsen, Andreas Muller

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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    Abstract

    he MAD (Multivariate Alteration Detection) method [1] is used to detect change between two HyMap scenes
    recorded during the DAISEX campaign in 1999 over the Barrax area in Spain near the city of Albacete. Out of a
    series of acquisitions two scenes recorded on 3 June at 12:00 and 4 June at 15:00 were selected for analysis. The
    Barrax experiment was undertaken in support of a future ESA Land Surface Mission (SPECTRA). A series of
    flights have been conducted over the same test area at different times of the day in order to maximize BRDF effects
    in various surface types.
    The changes observed by MAD in the two selected scenes are primarily due to the differences in flight
    directions (3 June N-S, 4 June E-W ) and sun angle changes. In higher MAD bands differences of the two scenes
    can be observed that can be related to irrigation practices. MAD can also be used to highlight remaining
    geometrical co-registration errors.
    The MAD method is based on the technique of canonical correlation analysis which is an established method in
    multivariate statistics. The MAD method finds differences between linear combinations of the spectral bands from
    the two acquisitions. These differences are orthogonal and they are constructed so that they explain maximum
    variance which is a healthy criterion for a change detector. Finding maximum variance in differences of linear
    combinations correspond to finding minimum correlation between these linear combinations. It is easy to show that
    the MAD variables are invariant to affine transformations of the input variables, i.e., the spectral bands. The
    method is therefore insensitive to any pre-processing that changes the digital numbers in a linear fashion. So
    whether one performs for example a linear (or an affine) relative normalization of one acquisition to the other
    doesn’t matter. This scaling invariance is a great advantage of the MAD method over other multivariate change
    detectors and it can be used to identify observations that are no-change pixels over time. These time invariant
    pixels are well suited for use in a relative normalization between time points. This relative normalization is useful
    if one desires to use other change detectors than MAD such as change detectors based on simple difference imagery
    and if atmospheric calibration cannot be performed (for example if work is done on historical data). Also,
    normalization is important if one wants to study the temporal development of a vegetation index or similar.
    For spectral data the variables (the spectral bands) are typically strongly correlated or collinear. This may lead
    to ill-conditioned, i.e., (near) singular variance-covariance matrices. Also, one might wish to smooth the elements
    of the eigenvectors (seen as functions of the wavelength) for improved interpretability. For hyperspectral data the
    number of observations may be low (relative to the number of variables). Again, this may lead to ill-conditioning.
    A possible remedy is regularization.
    MAD is seen as an important method in future automated change detection applications and automated relative
    normalization for multi- and hyperspectral satellite remote sensing systems.
    Original languageEnglish
    Title of host publicationAbstracts from the 3rd EARSeL Workshop on Imaging Spectroscopy
    PublisherEARSeL
    Publication date2003
    Publication statusPublished - 2003
    Event3rd EARSeL Workshop on Imaging Spectroscopy - Oberpfaffenhofen, Germany
    Duration: 13 May 200316 May 2003
    Conference number: 3

    Workshop

    Workshop3rd EARSeL Workshop on Imaging Spectroscopy
    Number3
    Country/TerritoryGermany
    CityOberpfaffenhofen
    Period13/05/200316/05/2003

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